Page 1

CONFERENCE HANDBOOK

VIENNA UNIVERSITY  OF  TECHNOLOGY   3-­‐5  APRIL  2013


2


EvoStar overview  programme EvoStar 2013 Overview Programme Wednesday 3 April

room 1

0900

Registration Desk opens

0930-0945

Conference opening and announcements

0945-1045

Plenary invited talk:

1045-1120

Coffee break EvoCOP 1 : Algorithmic Techniques

EvoBIO 1 Gene Expression

chairs: Blum, Middendorf 1. Bezerra, Lopez, Stuetzle 2. Benlic, Hao 3. Saraiva, Nepomuceno, Pinheiro 4. Chalupa

1. Granizo, Moore 2. Orsenigo, Vercellis 3. Pan, Hu, Malley, Andrew, Kargas, Moore 4. Sivley, Fish, Bush

EuroGP 2 : Analyses

EvoCOP 2 : Applications

EvoBIO 2: Comp Methods

chair: Banzhaf

Chair: Ventresca

chair: Giacobini

1. Seaton, Miller, Clarke 2. Hu, Moore, Banzhaf 3. Luis, Dos Santos 4. Fagan, Hembert, Oneill, McGarraghy

1. Reimann, Leal 2. Ventresca, Ombuki, Runka 3. Rubio-Largo, Vega-Rodríguez 4. Xiao, Chu, Lu, Ye, Liu, Cui

1. Salama, Freitas 2. Ahmed, Zhang, Peng 3. Whigham, Dick, Wright, Spencer 4. Silva, Vanneschi

EuroGP 1 : BPs 1 1. Nguyen, Zhang, Johnston, Tan 2. Sandoval-Perez, Becerra, Vanegas, Restrepo, Nino 3. Vanneschi, Castelli, Manzoni, Silva

1120-1300

1. De Falco, Laskowski, Olejnik, Scafuri, Tarantino, Tudruj 2. Folino, Pisani

1630-1830

EuroGP posters 1. Pham, Nguyen, Nguyen, O'Neill 2. Hogan, Arbuckle, Ryan 3. Harada, Takadama 4. Czajkowski, Kretowski 5. Cano, Zafra, Gibaja, Ventura

EvoRobot posters 1. Stradner, Hamann, Schwarzer, Michiels, Schmickl 2. Moshaiov, Zadok 3. Seo, Hyun

EuroPROJECTS posters: ADVANCE, ASHICS, AssisiBF, BioBoost, CoCoRo, EPiCS, FoCAS, LOGICAL, MIBISOC, MUSES, NASCENCE, SYMBRION

EvoINDUSTRY poster

EvoCOP poster

1. Li, Hao, Chen, Zhang, Peng

1. Liu, Han

1830-1930

Free time

1930-2100

EvoStar conference reception : Vienna city hall

Thursday 4 April

room 2

room 3

EvoENERGY 1

EvoBIO 3: BPs

Chair: 1. Pitzer, Beham, Affenzeller 2. Eliahou, Fonlupt, Fromentin 3. Abbasian, Mouhoub 4. Arbelaez, Codognet

EvoCOMPLEX 1

EvoFIN 1

Chairs: Cotta, Schaefer

chairs Tettamanzi , Agapitos

1. Gajda-Zagórska 2. Seo, Hyeon, Hyun, Lee 3. Pisarski, Rugala, Byrski, Kisiel

1. Loginov, Heywood 2. Mayo 3. Gabrielsson, Koenig, Johansson

EvoCOMPLEX 2 Chairs: Cotta, Schaefer 1. Boton, Castrillo, Vega 2. Antony, Wu, Szeto 3. Tonda, Lutton, Squillero, Wuillemin

EvoFIN 2 chairs Tettamanzi , Agapitos 1. Hochreiter, Krottendorfer 2. Michalak, Filipiak, Lipinski 3. Contreras, Hidalgo, Nunez

chair: Vanneschi 1. Tan, Grant, Whitfield, Greene 2. Gonzalez, Vega, Gomez, Sanchez 3. Darabos, Desai, Cowper, Giacobini, Graham, Lupien, Moore

EvoPAR poster 1. Wilson, Veeramachaneni, O'Reilly

EvoRISK poster

EvoGAMES posters 1. Winder 2. Maestro, Merelo, Salcedo

1. Alexander, Klegg

EvoMUSART posters

EvoIASP posters

1. Kramann 1. Pilic, Richter 2. Majid, Bishop 2. Xue, Zhang, Browne 3. Majid, Bishop 3. Silva, Ingalalli, Vinga, Carreiras, Melo, 4. Correia, Machado, Romero, Carballal Castelli, Vanneschi, Goncalves, Caldas 5. Guo, Tharib, Chang, Zhang

room 4

room 5

EvoPAR

EvoIASP 1 BPs

chairs: Fernandez, Merelo

chairs Cagnoni, Zhang

1. Turner-Baggs, Heywood 2. Garcia, Trujillo, Fernandez, Merelo, Olague 3. Derby, Veeramachaneni, O'Reilly

1. Amelio, Pizzuti 2. De Falco, Della Cioppa, Maisto, Scafuri, Tarantino 3. Fu, Johnston, Zhang

room 4

room 5

Coffee break room 1

EvoENERGY 2 & EvoINDUSTRY 1 chairs: Diwold, Glette, Sim

1135-1315

room 2

room 3

EvoCOP 4 : Multi-objective Optimisation Chair: Schoenauer

1. Flasch, Friese, Vladislavleva et al 2. Egarter, Sobe, Elmenreich 3. Etemadi, Kharma, Grogono

1. Suciu, Pallez, Cremene, Dumitrescu 2. Cervante, Xue, Shang, Zhang 3. Khouadjia, Schoenauer, Vidal, Dreo, Saveant 4. Marinakis, Marinaki

EuroGP 3 : Techniques

EvoCOP 5 : Hyperheuristics

EvoCOMNET 1 chair: Della Cioppa 1. Bucur, Iacca, Squillero, Tonda 2. Rubio, Vega 3. Sanseverino, Di Silvestre, Gallea

EvoMUSART 1 : Interactivity

EvoIASP 2

chair: McDermott

chairs Cagnoni, Zhang

1. Kaliakatsos, Floros, Vrahatis 2. Rafael, Affenzeller, Wagner 3. Nairat, Dahlstedt, Nordahl 4. Liapis, Yannakakis, Togelius

1. Krawiec, Nawrocki 2. Xie, Song, Ciesielski 3. Fu, Johnston, Zhang

Lunch

1430-1610

chair: O'Neill

Chair: Raidl

1. Goldman, Punch 2. Fonlupt, Robilliard 3. McDermott, Carroll 4. Naredo, Trujillo, Martinez

1. Kheiri, Ozcan 2. Asta, Ozcan, Parkes, Etaner 3. Infuehr, Raidl

EvoCOMNET 2 chair: DeFalco 1. Berrocal, Vega, Sanchez, Gomez 2. Schleich, Danoy, Dorronsoro, Bouvry 3. Monica, Ferrari

EvoMUSART 2 Aesthetics

EvoIASP 3

chair: Romero

chair: Song

1. Ciesielski, Barile, Trist 2. Janssen, Kaushik 3. McCormack 4. den Eeijer

1. Castelli, Silva, Vaanneschi, Cabral, Basconcelos, Catarino, Carreiras 2. Pan, Yang 3. Breaban

Coffee break EuroGP 4: BPs 2 chairs: Krawiec, Moraglio 1. Otero, Johnson 2. Goncalves, Silva 3. Dick, Whigham

1630-1810

1815 onward

EvoBIO posters 1. Castalsi, Maccagnola, Mari, Archetti 2. Fisher, Andrews, Kiralis, Sinnot, Moore 3. Rosenthal, ElSourani, Borschbach 4. Sulovari, Kiralis, Moore 5. Zagorski 6. Gaudesi, Marion, Musner, Squillero, Tonda 7. Manning, Walsh 8. Santander, Vega 9. Sharma, Gedeon

EvoCOP 3 : Theory & Parallelisation

chairs: Diwold, Glette

1610-1630

chair: Bush

room 1

1.Soares, Gomes, Antunes, Cardoso 2. Hutterer, Affenzeller, Auinger 3. Krueger, Wagner, Collet

0930-1110

1315-1430

room 5

Coffee break EvoCOMNET posters

1110-1135

room 4

Lunch

1430-1610

1610-1630

room 3

“Let's get physical: the future of evolutionary computing” by A E Eiben

chairs: Krawiec, Moraglio

1300-1430

room 2

EvoNUM - EvoRISK chairs: Esparcia 1. Wessing 2. Gieseke, Kramer 3. Joshi, Deshpande 4. Haddadi, Kayacik, Zincir-Heywood, Heywood

EvoTRANSFER

EvoMUSART 3: BPs

EvoGAMES 1

chair: Tettamanzi

chair: Machado

1. Moshaiov 2. Kronberger 3. Beham 4. Hutterer

1. Garcia, Trujillo, Fernandez, Merelo, Olague 2. Eisenmann, Lewis, Parent 3. Reed

1. Font, Mahalmann, Manrique, Togelius 2. Liapis, Yannakakis, Togelius 3. Lara, Cotta, Fernandez

room 4

room 5

chair: Burrelli, Merelo

Conference dinner : coaches depart from conference venue to Stift Klosterneuburg

Friday 5 April 0930-1110

room 1

room 2

room 3

EuroGP 5 : Applications

EvoCOP 6: BPs

EvoROBOT

chair: Johnson

Chairs: Blum, Middendorf

chairs: Haasdijk, Eiben

1. Drake, Kililis, Ozcan 2. Hong, Woodward, Li, Ozcan 3. Luna, Romero, Romero, Ventura 4. Agapitos, ONeill, Brabazon

1. Maenhout, Vanhoucke 2. Rainer, Papezek, Hu, Raidl 3. Nagata, Ono

1. Cerny, Kubalik 2. Perez-Moneo, Rossi 3. Noskov, Haasdijk, Weel, Eiben 4. Lee, Yosinski, Glette, Lipson, Clune

EvoSTOC chair: Simões 1. Mavrovouniotis, Yang 2. Fu, Sendhoff, Tang, Yao 3. Kiraz, Etaner, Ozcan

EvoGAMES 2 chairs: Burrelli, Merelo 1. Cook, Colton, Raad, Gow 2. Togelius

1110-1130

Coffee break

1130-1230

Plenary invited talk: "EvoConnectionism: The Algorithmic Capabilities of Adaptive Interactions Within and Between Evolving Entities" by Richard A. Watson

1230-1300

Conference closing, best paper awards, announcements about 2014

1300-1400

Lunch

1415 onwards

Optional Vienna excursion in afternoon, departing from conference venue

3


Acknowledgements

EvoStar gratefully thank the following for their assistance : Kevin Sim, Institute for Informatics & Digital Information, Edinburgh Napier University for website support and logo design A. Şima Uyar, Computer Engineering Department, Istanbul Technical University for EvoStar Publicity

Marc Schoenauer from INRIA Saclay - Île-de-France for continued assistance in providing MyReview conference management system Local organiser Bin Hu assisted by Doris Dicklberger and Günther Raidl from the Algorithms and Data Structures Group, Institute of Computer Graphics and Algorithms, Vienna University of Technology

Programme Chairs of all EvoStar events Institute for Informatics & Digital Information at Edinburgh Napier University, UK for EvoStar coordination and financial administration

EvoStar Handbook prepared by Jennifer Willies, EvoStar Coordinator

4


Contents Welcome to EvoStar, welcome to Vienna . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-7 Conference venie & transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8-9 Invited speakers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-11 Best paper nominations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12-13 EvoStar posters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .14-15 EuroProjects posters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 EuroGP programme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17-22 EvoBIO programme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23-29 EvoCOP programme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30-35 EvoMUSART programme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36-39 EvoCOMNET programme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40-41 EvoCOMPLEX programme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42-43 EvoENERGY programme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44-45 EvoFIN programme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46-47 EvoGAMES programme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .48-49 EvoIASP programme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .50-53 EvoINDUSTRY programme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 EvoRISK/NUM programme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .55-58 EvoPAR programme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 EvoROBOT programme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60-61 EvoSTOC programme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 EuroPROJECTS (new this year) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63-69 EvoTRANSFER. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70-71 Equipment & WIFI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 Springerlink . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 EvoStar reception & conference dinner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 Optional social excursion on Friday . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 75 List of participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76-79 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80-84 5


Welcome to  EvoStar eurogp 16th European Conference on Genetic Programming (Alberto Moraglio, Krzysztof Krawiec, Ting Hu)

evobio 11th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Computational Biology (William S Bush, Mario Giacobini, Leonardo Vanneschi)

evocop 13th European Conference on Evolutionary Computation in Combinatorial Optimization (Martin Middendorf, Christian Blum)

evomusart 2nd International Conference (& 11th European Event) on Evolutionary and Biologically Inspired Music, Sound, Art and Design (Penousal Machado, James McDermott, Adrian Carballal)

evoapplications European Conference on the Applications of Evolutionary Computation (Anna I Esparcia-Alcázar, EvoAPP Coordinator) incorporating 12 tracks:

evocomnet

Nature-inspired Techniques for Communication Networks and other Parallel and Distributed Systems (Antonio Della Cioppa. Ivanoe De Falco, Ernesto Tarantino)

evocomplex

Evolutionary algorithms and complex systems (Carlos Cotta, Robert Schaefer)

evoenergy

Evolutionary Algorithms in Energy Applications (Konrad Diwold, Kyrre Glette)

evofin

Evolutionary computation in finance and economics (Andrea Tettamanzi, Alexandros Agapitos)

evogames

Bio-inspired algorithms in games (Paolo Burrelli, J J Merelo Guervós)

evoiasp

Evolutionary computation in image analysis, signal processing and pattern recognition (Stefano Cagnoni,Mengjie Zhang)

evoindustry

Nature-Inspired Techniques in Industrial Settings (Neil Urquhart, Kevin Sim)

evonum

Bio-inspired algorithms for continuous parameter optimization (Anna Esparcia-Alcázar, Anikó Ekárt)

evopar

Parallel architectures and distributed Infrastructures (Francisco Fernandez de Vega, J J Merelo Guervós)

evorisk

Computational intelligence for risk management, security and defence applications (Anna Esparcia-Alcázar,, Sara Silva)

evorobot

Evolutionary computation in robotics (Evert Haasdijk, Gusz Eiben) evostoc evolutionary algorithms in stochastic and dynamic environments (Anabela Simões, Philipp Rohlfshagen) also featuring

evotransfer

Technology transfer event on evolutionary computation (Andrea Tettamanzi)

europrojects

European projects relevant to evolutionary and bio-inspired research (Anna Esparcia-Alcázar)

6


Welcome to  Vienna On behalf of all the EvoStar 2013 organisers, we are pleased to see you in Vienna for the five co-located EvoStar conferences, a total of 16 excellent events and with 145 papers presented over two and a half days and with EvoStar now in its 16th year. We are convinced this year's EvoStar again has an exciting program with many high-quality contributions in the diverse fields of bio-inspired computation, and the events provide excellent opportunities to meet friends and establish new working relationships. We are also pleased to include two great invited talks in the programme from A E Eiben and Richard Watson. The Vienna University of Technology welcomes you and is pleased to act as host for EvoStar 2013. While you are in Vienna, please be sure to enjoy the conference but also the city. Vienna has a lot to offer : good sightseeing, wonderful culture and great food. Besides the technical program, we have put put together a nice mix of social activities for you over the three days including a welcome reception on Wednesday evening which will take place in the gorgeous city hall, the Rathaus. For the conference dinner on Thursday, we shall visit the famous Klosterneuburg monastery and enjoy a nice meal in the Stiftskeller. And for those staying on Friday afternoon, we have organised an optional and rather unconventional city tour. You can see more details on page 75. Do not miss these opportunities! If you would like to know more about Vienna or need any help, do not hesitate to ask at the conference desk or any of the local organisers. We wish you a pleasant and enjoyable stay in Vienna. Local Organisers: Bin Hu, Doris Dicklberger, Günther Raidl and the Algorithms and Data Structures Group from the Vienna University of Technology

and EvoStar Coordinator: Jennifer Willies 7


Conference Venue Evostar 2013 is held in Freihaus of the Vienna University of Technology at Wiedner Hauptstraße 8-10, 1040 Vienna. The university building is very central and the nearest public transport station Karlsplatz can be reached via underground lines U1 (red), U2 (purple), and U4 (green) Vienna has an extended network of public transport consisting of 5 metro lines as well as 28 tram lines and 90 bus routes, see Wiener Linien (www.wienerlinien.at). A range of 24-, 48-, 72-hour tickets are available as well as Vienna Cards which combine transport and tourism. Major sightseeing spots in the city center such as the famous shopping street Kärntner Straße, St. Stephen's Cathedral or Hofburg are reachable by a 15 minutes walk. See www.wien.info for useful information.

*

8


Transport  Links

9


Invited speakers Professor A E Eiben     Opening Talk: Wednesday, 3 April at 09:45 am “Let's get physical: the future of evolutionary computing”

Evolution is one of the major powers in the universe that has been studied for about two centuries. Computers, invented in the 20th century, made it possible to move from passively understanding to actively using evolutionary processes as tools in digital spaces. The related area is called Evolutionary Computing. I argue that in the 21st century (probably in the near future) it will be possible to implement and utilize artificial evolutionary processes outside such imaginary spaces and make them physically embodied. In other words, I envision the “Evolution of Things'', rather than just the evolution of digital objects, leading to a new field of Embodied Artificial Evolution. In this talk I will present this vision in more detail and explain why these developments will radically change our lives. Gusz Eiben is Professor and Head of the Computational Intelligence Group in the Department of Computer Science at the Vrije Universiteit Amsterdam. He is one of the European early birds of Evolutionary Computing, with his first paper on the subject dating back to 1989. Since then he has published over 100 research papers, and co-authored the first comprehensive introductory book on evolutionary computing, entitled Introduction to Evolutionary Computing (Springer, 2003, with J.E. Smith). Gusz's research interests in evolutionary computing range from fundamental issues such as reproduction operators, self-calibrating algorithms, and constraint handling, to applications in data mining, evolutionary art, the simulation of emergent artificial societies, evolutionary robotics, and more recently, embodied artificial evolution. Gusz has been an organizing committee member for virtually all the major international conferences on evolutionary computing (CEC, EP, EuroGP, FOGA, GECCO, PPSN), and is on the editorial board of five international journals (including JEC, IEEE TEC, GPEH). He is also a series editor for Springer's book series on Natural Computing, and a member of numerous science management bodies, including the IEEE Computer Society Technical Committee on Computational Intelligence, the Executive Board of the European Network of Excellence on Evolutionary Computing, and the Steering Committee for the Parallel Problem Solving from Nature (PPSN) conference series. He presented a TED talk Tech Kangaroos: Evolution at Work" in 2011. www.cs.vu.nl/~gusz/

10


Invited Speakers Dr Richard Watson Closing Talk: Friday, 5 April at 11:30 am “EvoConnectionism: The Algorithmic Capabilities of Adaptive Interactions Within and Between Evolving Entities” This talk discusses a developing body of work characterising a functional equivalence between learning and evolution. This is based on the observation that the selective pressures acting on biotic relationships in evolving systems change fitness interactions in the same way as simple associative learning rules that are well-understood in the context of neural networks. This enables conceptual and specific mechanistic knowledge from cognitively-inspired machine learning research to be applied to surprisingly diverse fields of evolutionary biology, including: evo-devo, evo-eco, social evolution and the major transitions in evolution. Here we describe how some of the recent results derived from this equivalence fit together to form the basis of a unified theory of ‘evo-connectionism’. In particular, we discuss the potential of individual selection to facilitate the formation of higher-level units of selection during an evolutionary transition via the same principles by which unsupervised associative learning can beneficially prime performance at supervised learning tasks. Richard A. Watson is a senior lecturer in the natural systems research group at the University of Southampton's School of Electronics and Computer Science. He received his BA in AI from the University of Sussex in 1990 and then worked in industry for about five years. Returning to academia, he chose Sussex again for an MSc in knowledge-based systems, where he was introduced to evolutionary modeling. His PhD in computer science at Brandeis University (2002) resulted in 22 publications and a dissertation addressing the algorithmic concepts underlying the major transitions in evolution. A postdoctoral position at Harvard University's Department of Organismic and Evolutionary Biology provided training to complement his computer science background. He now has over 50 journal and conference publications on topics spanning artificial life, robotics, evolutionary computation, population genetics, neural networks and computational biology. At Southampton, he's building his research programme and leading preparation of a new MSc in complexity science. He is the author of Compositional Evolution: The Impact of Sex, Symbiosis, and Modularity on the Gradualist Framework of Evolution (MIT Press, 2006). http://www.ecs.soton.ac.uk/people/raw

11


Best paper  nominaPons These papers have been nominated in their categories for EvoStar Best Paper Awards in 2013. Winners will be announced at the conference closing ceremony on Friday, 5 April at 12:30.

EuroGP Best Paper Nominations A Multi-objective Optimization Energy Approach to Predict the Ligand Conformation in a Docking Process Angelica Sandoval-Perez, David Becerra, Diana Vanegas, Daniel Restrepo-Montoya, Fernando Nino A New Implementation of Geometric Semantic GP and its Application to Problems in Pharmacokinetics Leonardo Vanneschi, Mauro Castelli, Luca Manzoni, Sara Silva Automated Problem Decomposition for the Boolean Domain with Genetic Programming Fernando Otero, Colin Johnson Balancing Learning and Overfitting in Genetic Programming with Interleaved Sampling of Training Data Ivo Gonçalves, Sara Silva Controlling Bloat through Parsimonious Elitist Replacement and Spatial Structure Grant Dick, Peter Whigham Learning Reusable Initial Solutions for Multi-objective Order Acceptance and Scheduling Problems with Genetic Programming Su Nguyen, Mengjie Zhang, Mark Johnston, Kay Chen Tan

EvoBIO Best Paper Nominations in association with Inferring Human Phenotype Networks from Genome-Wide Genetic Associations Christian Darabos, Kinjal Desai, Richard Cowper-Sallari, Mario Giacobini, Britney E. Graham, Mathieu Lupien, Jason H. Moore Hybrid Multiobjective Artificial Bee Colony with Differential Evolution Applied to Motif Finding David L. González-Álvarez, Miguel A. Vega-Rodríguez, Juan A. Gómez-Pulido, Juan M. Sánchez-Pérez Time-point Specific Weighting Improves Coexpression Networks from Time-course Experiments Jie Tan, Gavin Grant, Michael Whitfield, Casey Greene

12


Best paper  nominaPons EvoCOP Best Paper Nominations An Artificial Immune System based approach for solving the Nurse Re-Rostering Problem Broos Maenhout, Mario Vanhoucke Balancing Bicycle Sharing Systems: A Variable Neighborhood Search Approach Marian Rainer-Harbach, Petrina Papazek, Bin Hu, Günther R. Raidl High-Order Sequence Entropies for Measuring Population Diversity in the Traveling Salesman Problem Yuichi Nagata, Isao Ono

EvoMUSART Best Paper Nominations Aesthetic Measures for Evolutionary Vase Design Kate Reed EvoSpace-Interactive: A Framework to Develop Distributed Collaborative-Interactive Evolutionary Algorithms for Artistic Design Mario Garcia-Valdez, Leonardo Trujillo, Francisco Fernández de Vega, Juan Julián Merelo Guervós, Gustavo Olague Inverse Mapping with Sensitivity Analysis for Partial Selection in Interactive Evolution Jonathan Eisenmann, Matthew Lewis, Rick Parent

EvoIASP Best Paper Nominations Adding Chaos to Differential Evolution for Range Image Registration Ivanoe De Falco, Antonio Della Cioppa, Domenico Maisto, Umberto Scafuri, Ernesto Tarantino A Genetic Algorithm for Color Image Segmentation Alessia Amelio, Clara Pizzuti Automatic Construction of Gaussian-Based Edge Detectors Using Genetic Programming Wenlong Fu, Mark Johnston, Mengjie Zhang

EvoCOMNET Best Paper Nominations Impact of the Number of Beacons in PSO-Based Auto-localization in UWB Networks Stefania Monica, Gianluigi Ferrari Pareto-optimal Glowworm Swarms Optimization for Smart Grids Management Eleonora Riva Sanseverino, Maria Luisa Di Silvestre, Roberto Gallea 13


EvoStar Poster  session Wednesday 3 April 1630-1830

Combined EvoStar poster session

The EvoStar poster session will take place on Wednesday afternoon from 1630-1830. Detailed abstracts are listed on the corresponding event pages. Poster boards are portrait configuration, 200 cm high by 100 cm wide, and can be affixed by tape, blutac or clips. Posters can be mounted after 1500 on Wednesday with authors free to choose their own position. Posters t should be taken down by Wednesday evening. Poster tubes can be stored at the conference desk. The following posters will be presented: A Framework for Modeling Automatic Offloading of Mobile Applications Using Genetic Programming Gianluigi Folino, Francesco Sergio Pisani A Grammar-Guided Genetic Programming Algorithm for Multi-Label Classification Alberto Cano, Amelia Zafra, Eva L. Gibaja, Sebastián Ventura An Evolutionary Approach for Automatic Seedpoint Setting in Brain Fiber Tracking Tobias Pilic, Hendrik Richter An Evolutionary Approach to Wetlands Design Marco Gaudesi, Andrea Marion, Tommaso Musner, Giovanni Squillero, Alberto Tonda A Multiobjective Proposal Based on the Firefly Algorithm for Inferring Phylogenies Sergio Santander-Jiménez, Miguel A. Vega-Rodríguez Asynchronous Evaluation based Genetic Programming: Comparison of Asynchronous and Synchronous Evaluation and its Analysis Tomohiro Harada, Keiki Takadama Biologically–inspired Motion Pattern Design of Multi–legged Creatures Shihui Guo, Safa Tharib, Jian Chang, Jianjun Zhang Cell-based Metrics Improve the Detection of Gene-Gene Interactions using Multifactor Dimensionality Reduction Jonathan M. Fisher, Peter Andrews, Jeff Kiralis, Nicholas A. Sinnott-Armstrong, Jason H. Moore Cloud Scale Distributed Evolutionary Strategies for High Dimensional Problems Dennis Wilson, Kalyan Veeramachaneni, Una-May O’Reilly Comparing Evolutionary Algorithms to Solve the Game of Mastermind Javier Maestro-Montojo, Juan Julián Merelo-Guervós, Sancho Salcedo-Sanz Darwinian Pianos: Realtime Composition based on Competitive Evolutionary Process Guido Kramann Dynamic Evolutionary Membrane Algorithm in Dynamic Environments Chuang Liu, Min Han Emergence of motifs in model gene regulatory networks Marcin Zagórski Evolving Counter-Propagation Neuro-controllers for Multi-objective Robot Navigation Amiram Moshaiov, Michael Zadok Examining the Diversity Property of Semantic Similarity based Crossover Tuan Anh Pham, Quang Uy Nguyen, Xuan Hoai Nguyen, Michael O'Neill Feature Selection and Novelty in Computational Aesthetics João Correia, Penousal Machado, Juan Romero, Adrian Carballal

14


EvoStar Poster  session Generating Artificial Neural Networks for Value Function Approximation in a Domain Requiring a Shifting Strategy Ransom K. Winder Global Top-Scoring Pair Decision Tree for Gene Expression Data Analysis Marcin Czajkowski, Marek Kretowski How Early and with How Little Data? Using Genetic Programming to Evolve Endurance Classifiers for MLC NAND Flash Memory Damien Hogan, Tom Arbuckle, Conor Ryan Hybrid Genetic Algorithms for Stress Recognition in Reading Nandita Sharma, Tom Gedeon Impact of Different Recombination Methods in a Mutation-Specific MOEA for a Biochemical Application Susanne Rosenthal, Nail El-Sourani, Markus Borschbach Improving the Performance of CGPANN for Breast Cancer Diagnosis using Crossover and Radial Basis Functions Timmy Manning, Paul Walsh Load Balancing in Distributed Applications Based on Extremal Optimization Ivanoe De Falco, Eryk Laskowski, Richard Olejnik, Umberto Scafuri, Ernesto Tarantino, Marek Tudruj Mining for Variability in the Coagulation Pathway: A Systems Biology Approach Davide Castaldi, Daniele Maccagnola, Daniela Mari, Francesco Archetti Multi-Objective Optimizations of Structural Parameter Determination for Serpentine Channel Heat Sink Xuekang Li, Xiaohong Hao, Yi Chen, Muhao Zhang, Bei Peng Novel Initialisation and Updating Mechanisms in PSO for Feature Selection in Classification Bing Xue, Mengjie Zhang, Will N. Browne Optimal Use of Biological Expert Knowledge from Literature Mining in Ant Colony Optimization for Analysis of Epistasis in Human Disease Arvis Sulovari, Jeff Kiralis, Jason H. Moore Prediction of Forest Aboveground Biomass: An Exercise on Avoiding Overfitting Sara Silva, Vijay Ingalalli, Susana Vinga, João M.B. Carreiras, Joana B. Melo, Mauro Castelli, Leonardo Vanneschi, Ivo Gonçalves, José Caldas Searching for Risk in Large Complex Spaces Kester Clegg, Rob Alexander Swarmic Paintings and Colour Attention Mohammad Majid al-Rifaie, Mark Bishop Swarmic Sketches and Attention Mechanism Mohammad Majid al-Rifaie, John Mark Bishop Toward Automatic Gait Generation for Quadruped Robots Using Cartesian Genetic Programming Kisung Seo, Soohwan Hyun Virtual Spatiality in Agent Controllers: Encoding Compartmentalization Jürgen Stradner, Heiko Hamann, Christopher S.F. Schwarzer, Nico K. Michiels, Thomas Schmickl

15


EuroPROJECTS Â Posters Wednesday 3 April 1630-1830 session

EuroPROJECTS at EvoStar poster

EvoStar invited abstracts from European-funded projects where research is related to EvoStar’s thematic areas. These will be presented during the poster session on Wednesday, 3 April 16:30-18:30 and abstracts are on page 63-69. You are invited to view the research developments and meet project representatives from:

ADVANCE : Advanced predictive-analysis-based decision-support engine for logistics ASHICS : Automating the Search for Hazards in Complex Systems AssisiBF : Animal and robot Societies Self-organise and Integrate by Social Interaction (bees and fish BioBoost : Biomass Based Energy Intermediates Boosting Biofuel Production CoCoRo : Collective Cognitive Robots EPiCS : Engineering Proprioception in Computing Systems FoCAS : Fundamentals of Collective Adaptive Systems: FoCAS Organisation, Coordination and Support LOGICAL : transnational logistics improvement through cloud computing and innovative cooperative business models MIBISOC : Medical Imaging Using Bio-inspired and Soft Computing MUSES : Multiplatform Usable Endpoint Security NASCENCE : NAnoSCale Engineering for Novel Computation using Evolution SYMBRION : Symbiotic evolutionary robot organisms

16


EuroGP Â programme Wednesday 3 April 1120-1300

Room 1

EuroGP1: Best Paper Nominees 1 Chairs: Krzysztof Krawiec, Alberto Moraglio Learning Reusable Initial Solutions for Multi-objective Order Acceptance and Scheduling Problems with Genetic Programming (EuroGP Best Paper Candidate) Su Nguyen, Mengjie Zhang, Mark Johnston, Kay Chen Tan Order acceptance and scheduling (OAS) is an important issue in make-to-order production systems that decides the set of orders to accept and the sequence in which these accepted orders are processed to increase total revenue and improve customer satisfaction. This paper aims to explore the Pareto fronts of trade-off solutions for a multi-objective OAS problem. Due to its complexity, solving this problem is challenging. A two-stage learning/optimising (2SLO) system is proposed in this paper to solve the problem. The novelty of this system is the use of genetic programming to evolve a set of scheduling rules that can be reused to initialise populations of an evolutionary multi-objective optimisation (EMO) method. The computational results show that 2SLO is more effective than the pure EMO method. Regarding maximising the total revenue, 2SLO is also competitive as compared to other optimisation methods in the literature. A Multi-objective Optimization Energy Approach to Predict the Ligand Conformation in a Docking Process (EuroGP Best Paper Candidate) Angelica Sandoval-Perez, David Becerra, Diana Vanegas, Daniel Restrepo-Montoya, Fernando Nino This work proposes a multi-objective algorithmic method for modeling the prediction of the conformation and configuration of ligands in receptor-ligand complexes by considering energy contributions of molecular interactions. The proposed approach is an improvement over others in the field, where the principle insight is that a Pareto front helps to understand the tradeoffs in the actual problem. The method is based on three main features: (i) Representation of molecular data using a trigonometric model; (ii) Modeling of molecular interactions with all-atoms force field energy functions and (iii) Exploration of the conformational space through a multi-objective evolutionary algorithm. The performance of the proposed model was evaluated and validated over a set of well known complexes. The method showed a promising performance when predicting ligands with high number of rotatable bonds. A New Implementation of Geometric Semantic GP and its Application to Problems in Pharmacokinetics (EuroGP Best Paper Candidate) Leonardo Vanneschi, Mauro Castelli, Luca Manzoni, Sara Silva Moraglio et al. have recently introduced new genetic operators for genetic programming, called geometric semantic operators. These operators induce a unimodal fitness landscape for all the problems consisting in matching input data with known target outputs (like regression and classification). This feature facilitates genetic programming evolvability, which makes these operators extremely promising. Nevertheless, Moraglio et al. leave open problems, the most important one being the fact that these operators, by construction, always produce offspring that are larger than their parents, causing an exponential growth in the size of the individuals, which actually renders them useless in practice. In this paper we overcome this limitation by presenting a new efficient implementation of the geometric semantic operators. This allows us, for the first time, to use them on complex real-life applications, like the two problems in pharmacokinetics that we address here. Our results confirm the excellent evolvability of geometric semantic operators, demonstrated by the good results obtained on training data. Furthermore, we have also achieved a surprisingly good generalization ability, a fact that can be explained considering some properties of geometric semantic operators, which makes them even more appealing than before.

17


EuroGP Â programme Wednesday 3 April 1430-1610

Room 1

EuroGP2: Analyses Chair: Wolfgang Banzhaf Semantic Bias in Program Coevolution Tom Seaton, Julian F. Miller, Tim Clarke We investigate two pathological coevolutionary behaviours, disengagement and cycling, in GP systems. An empirical analysis is carried out over constructed GP problems and the Game of Tag, a historical pursuit and evasion task. The effects of semantic bias on the occurrence of pathologies and consequences for performance are examined in a coevolutionary context. We present findings correlating disengagement with semantic locality of the genotype to phenotype map using a minimal competitive coevolutionary algorithm. Robustness and Evolvability of Recombination in Linear Genetic Programming Ting Hu, Wolfgang Banzhaf, Jason H. Moore The effect of neutrality on evolutionary search has been recognized to be crucially dependent on its distribution at the phenotypic level. Quantitatively characterizing robustness and evolvability in genotype and phenotype spaces greatly helps to understand the influence of neutrality on Genetic Programming. Most existing robustness and evolvability studies focus on mutations with a lack of investigation of recombinational operations. Here, we extend a previously proposed quantitative approach of measuring mutational robustness and evolvability in Linear GP. By considering a simple LGP system that has a compact representation and enumerable genotype and phenotype spaces, we quantitatively characterize the robustness and evolvability of recombination at the phenotypic level. In this simple yet representative LGP system, we show that recombinational properties are correlated with mutational properties. Utilizing a population evolution experiment, we demonstrate that recombination significantly accelerates the evolutionary search process and particularly promotes robust phenotypes for innovative phenotypic explorations. On the Evolvability of a hybrid Ant Colony-Cartesian Genetic Programming Methodology Sweeney Luis, Marcus Vinicius dos Santos A method that uses Ant Colonies as a Model-based Search to Cartesian Genetic Programming (CGP) to induce computer programs is presented. Candidate problem solutions are encoded using a CGP representation. Ants generate problem solutions guided by pheromone traces of entities and nodes of the CGP representation. The pheromone values are updated based on the paths followed by the best ants, as suggested in the Rank-Based Ant System. To assess the evolvability of the system we applied a modifed version of a method introduced to measure rate of evolution. Our results show that such method effectively reveals how evolution proceeds under different parameter settings. The proposed hybrid architecture shows high evolvability in a dynamic environment by maintaining a pheromone model that elicits high genotype diversity. Understanding Expansion Order and Phenotypic Connectivity in piGE David Fagan, Erik Hemberg, Michael O'Neill, Sean McGarraghy Since its inception, piGE has used evolution to guide the order of how to construct derivation trees. It was hypothesised that this would allow evolution to adjust the order of expansion during the run and thus help with search. This research aims to identify if a specific order is reachable, how reachable it may be, and goes on to investigate what happens to the expansion order during a piGE run. It is concluded that within piGE we do not evolve towards a specific order but a rather distribution of orders. The added complexity that an evolvable order gives piGE can make it difficult to understand how it can effectively search, by examining the connectivity of the phenotypic landscape it is hoped to understand this. It is concluded that the addition of an evolvable derivation tree expansion order makes the phenotypic landscape associated with piGE very densely connected, with solutions now linked via a single mutation event that were not previously connected.

18


EuroGP Â programme Wednesday 3 April 1630-1830

EuroGP POSTERS

Examining the Diversity Property of Semantic Similarity based Crossover Tuan Anh Pham, Quang Uy Nguyen, Xuan Hoai Nguyen, Michael O'Neill Population diversity has long been seen as a crucial factor for the efficiency of Evolutionary Algorithms in general, and Genetic Programming (GP) in particular. This paper experimentally investigates the diversity property of a recently proposed crossover, Semantic Similarity based Crossover (SSC). The results show that while SSC helps to improve locality, it leads to the loss of diversity of the population. This could be the reason that sometimes SSC fails in achieving superior performance when compared to standard subtree crossover. Consequently, we introduce an approach to maintain the population diversity by combining SSC with a multi-population approach. The experimental results show that this combination maintains better population diversity, leading to further improvement in GP performance. Further SSC parameters tuning to promote diversity gains even better results. How Early and with How Little Data? Using Genetic Programming to Evolve Endurance Classifiers for MLC NAND Flash Memory Damien Hogan, Tom Arbuckle, Conor Ryan Despite having a multi-billion dollar market and many operational advantages, Flash memory suffers from a serious drawback, that is, the gradual degradation of its storage locations through use. Manufacturers currently have no method to predict how long they will function correctly, resulting in extremely conservative longevity specifications being placed on Flash devices. We leverage the fact that the durations of two crucial Flash operations, program and erase, change as the chips age. Their timings, recorded at intervals early in chips' working lifetimes, are used to predict whether storage locations will function correctly after given numbers of operations. We examine how early and with how little data such predictions can be made. Genetic Programming, employing the timings as inputs, is used to evolve binary classifiers that achieve up to a mean of 97.88% correct classification. This technique displays huge potential for real-world application, with resulting savings for manufacturers. Asynchronous Evaluation based Genetic Programming: Comparison of Asynchronous and Synchronous Evaluation and its Analysis Tomohiro Harada, Keiki Takadama This paper compares an asynchronous evaluation based GP with a synchronous evaluation based GP to investigate the evolution ability of an asynchronous evaluation on the GP domain. As an asynchronous evaluation based GP, this paper focuses on Tierra-based Asynchronous GP we have proposed, which is based on a biological evolution simulator, Tierra. The intensive experiment compares TAGP with simple GP by applying them to a symbolic regression problem, and it is revealed that an asynchronous evaluation based GP has better evolution ability than a synchronous one. Global Top-Scoring Pair Decision Tree for Gene Expression Data Analysis Marcin Czajkowski, Marek Kretowski Extracting knowledge from gene expression data is still a major challenge. Relative expression algorithms use the ordering relationships for a small collection of genes and are successfully applied for micro-array classification. However, searching for all possible subsets of genes requires a significant number of calculations, assumptions and limitations. In this paper we propose an evolutionary algorithm for global induction of top-scoring pair decision trees. We have designed several specialized genetic operators that search for the best tree structure and the splits in internal nodes which involve pairwise comparisons of the gene expression values. Preliminary validation performed on real-life micro-array datasets is promising as the proposed solution is highly competitive to other relative expression algorithms and allows exploring much larger solution space. A Grammar-Guided Genetic Programming Algorithm for Multi-Label Classification Alberto Cano, Amelia Zafra, Eva L. Gibaja, SebastiĂĄn Ventura Multi-label classification is a challenging problem which demands new knowledge discovery methods. This paper presents a Grammar-Guided Genetic Programming algorithm for solving multi-label classification problems using IF-THEN classification rules. This algorithm, called G3P-ML, is evaluated and compared to other multi-label classification techniques in different application domains. Computational experiments show that G3P-ML often obtains better results than other algorithms while achieving a lower number of rules than the other methods.

19


EuroGP Â programme Thursday 4 April 1430-1610

Room 1

EuroGP3: Techniques Chair: Michael O'Neill Reducing Wasted Evaluations in Cartesian Genetic Programming Brian Goldman, William Punch Cartesian Genetic Programming (CGP) is a form of Genetic Programming (GP) where a large proportion of the genome is identifiably unused by the phenotype. This can lead mutation to create offspring that are genotypically different but phenotypically identical, and therefore do not need to be evaluated. We investigate theoretically and empirically the effects of avoiding these otherwise wasted evaluations, and provide evidence that doing so reduces the median number of evaluations to solve four benchmark problems, as well as reducing CGP's sensitivity to the mutation rate. The similarity of results across the problem set in combination with the theoretical conclusions supports the general need for avoiding these unnecessary evaluations. PhenoGP: Combining Programs to Avoid Code Disruption Cyril Fonlupt, Denis Robilliard In conventional Genetic Programming (GP), n programs are simultaneously evaluated and only the best programs will survive from one generation to the next. It is a pity as some programs might contain useful code that might be hidden or not evaluated due to the presence of introns. For example in regression, 0 * (perfect code) will unfortunately not be assigned a good fitness and this program might be discarded due to the evolutionary process. In this paper, we develop a new form of GP called PhenoGP (PGP). PGP individuals consist of ordered lists of programs to be executed in which the ultimate goal is to find the best order from simple building-blocks programs. If the fitness remains stalled during the run, new building-blocks programs are generated. PGP seems to compare fairly well with canonical GP. Program Optimisation with Dependency Injection James McDermott, Paula Carroll For many real-world problems, there exist non-deterministic heuristics which generate valid but possibly sub-optimal solutions. The program optimisation with dependency injection method, introduced here, allows such a heuristic to be placed under evolutionary control, allowing search for the optimum. Essentially, the heuristic is "fooled" into using a genome, supplied by a genetic algorithm, in place of the output of its random number generator. The method is demonstrated with generative heuristics in the domains of 3D design and communications network design. It is also used in novel approaches to genetic programming. Searching for Novel Classifiers Enrique Naredo, Leonardo Trujillo, Yuliana MartĂ­nez Natural evolution is an open-ended search process without an a priori fitness function that needs to be optimized. On the other hand, evolutionary algorithms (EAs) rely on a clear and quantitative objective. The Novelty Search algorithm (NS) substitutes fitness-based selection with a \emph {novelty} criteria; i.e., individuals are chosen based on their uniqueness. To do so, individuals are described by the behaviors they exhibit, instead of their phenotype or genetic content. NS has mostly been used in evolutionary robotics, where the concept of behavioral space can be clearly defined. Instead, this work applies NS to a more general problem domain, classification. To this end, two behavioral descriptors are proposed, each describing a classifier's performance from two different perspectives. Experimental results show that NS-based search can be used to derive effective classifiers. In particular, NS is best suited to solve difficult problems, where exploration needs to be encouraged and maintained.

20


EuroGP Â programme Thursday 4 April

1630-1810

Room 1

EuroGP4: Best Paper Nominees 2 Chairs: Krzysztof Krawiec, Alberto Moraglio Automated Problem Decomposition for the Boolean Domain with Genetic Programming (EuroGP Best Paper Candidate) Fernando Otero, Colin Johnson Researchers have been interested in exploring the regularities and modularity of the problem space in genetic programming (GP) with the aim of decomposing the original problem into several smaller subproblems. The main motivation is to allow GP to deal with more complex problems. Most previous works on modularity in GP emphasise the structure of modules used to encapsulate code and/or promote code reuse, instead of in the decomposition of the original problem. In this paper we propose a problem decomposition strategy that allows the use of a GP search to find solutions for subproblems and combine the individual solutions into the complete solution to the problem. Balancing Learning and Overfitting in Genetic Programming with Interleaved Sampling of Training Data (EuroGP Best Paper Candidate) Ivo Gonçalves, Sara Silva Generalization is the ability of a model to perform well on cases not seen during the training phase. In Genetic Programming generalization has recently been recognized as an important open issue, and increased efforts are being made towards evolving models that do not overfit. In this work we expand on recent developments that showed that using a small and frequently changing subset of the training data is effective in reducing overfitting and improving generalization. Particularly, we build upon the idea of randomly choosing a single training instance at each generation and balance it with periodically using all training data. The motivation for this approach is based on trying to keep overfitting low (represented by using a single training instance) and still presenting enough information so that a general pattern can be found (represented by using all training data). We propose two approaches called interleaved sampling and random interleaved sampling that respectively represent doing this balancing in a deterministic or a probabilistic way. Experiments are conducted on three high-dimensional reallife datasets on the pharmacokinetics domain. Results show that most of the variants of the proposed approaches are able to consistently improve generalization and reduce overfitting when compared to standard Genetic Programming. The best variants are even able of such improvements on a dataset where a recent and representative state-of-the-art method could not. Furthermore, the resulting models are short and hence easier to interpret, an important achievement from the applications' point of view. Controlling Bloat through Parsimonious Elitist Replacement and Spatial Structure (EuroGP Best Paper Candidate) Grant Dick, Peter Whigham The concept of bloat --- the increase of program size without a corresponding increase in fitness --- presents a significant drawback to the application of genetic programming. One approach to controlling bloat, dubbed spatial structure with elitism (SS+E), uses a combination of spatial population structure and local elitist replacement to implicitly constrain unwarranted program growth. However, the default implementation of SS+E uses a replacement scheme that prevents the introduction of smaller programs in the presence of equal fitness. This paper introduces a modified SS+E approach in which replacement is done under a lexicographic parsimony scheme. The proposed model, spatial structure with lexicographic parsimonious elitism (SS +LPE), exhibits an improvement in bloat reduction and, in some cases, more effectively searches for fitter solutions.

21


EuroGP programme Friday 5 April 0930-1110

Room 1

EuroGP5: Applications Chair : Colin Johnson Generation of VNS Components with Grammatical Evolution for Vehicle Routing John Drake, Nikolaos Kililis, Ender Özcan The vehicle routing problem (VRP) is a family of problems whereby a fleet of vehicles must service the commodity demands of a set of geographically scattered customers from one or more depots, subject to a number of constraints. Early hyper-heuristic research focussed on selecting and applying a low-level heuristic at a given stage of an optimisation process. Recent trends have led to a number of approaches being developed to automatically generate heuristics for a number of combinatorial optimisation problems. Previous work on the VRP has shown that the application of hyper-heuristic approaches can yield successful results. In this paper we investigate the potential of grammatical evolution as a method to evolve the components of a variable neighbourhood search (VNS) framework. In particular two components are generated; constructive heuristics to create initial solutions and neighbourhood move operators to change the state of a given solution. The proposed method is tested on standard benchmark instances of two common VRP variants. Automated Design of Probability Distributions as Mutation Operators for Evolutionary Programming Using Genetic Programming Libin Hong, John Woodward, Jingpeng Li, Ender Özcan The mutation operator is the only source of variation in Evolutionary Programming. In the past these have been human nominated and included the Gaussian,Cauchy,and the Levy distributions. We automatically design mutation operators (probability distributions) using Genetic Programming. This is done by using a standard Gaussian random number generator as the terminal set and and basic arithmetic operators as the function set. In other words, an arbitrary random number generator is a function of a randomly (Gaussian) generated number passed through an arbitrary function generated by Genetic Programming. Rather than engaging in the futile attempt to develop mutation operators for arbitrary benchmark functions (which is a consequence of the No Free Lunch theorems), we consider tailoring mutation operators for particular function classes. We draw functions from a function class (a probability distribution over a set of functions). The mutation probability distribution is trained on a set of function instances drawn from a given function class. It is then tested on a separate independent test set of function instances to confirm that the evolved probability distribution has indeed generalized to the function class. Initial results are highly encouraging: on each of the ten function classes the probability distributions generated using Genetic Programming outperform both the Gaussian and Cauchy distributions. Discovering Subgroups by means of Genetic Programming José M. Luna, José R. Romero, Cristóbal Romero, Sebastián Ventura This paper deals with the problem of discovering subgroups in data by means of a grammar guided genetic programming algorithm, each subgroup including a set of related patterns. The proposed algorithm combines the requirements of discovering comprehensible rules with the ability of mining expressive and flexible solutions thanks to the use of a context-free grammar. A major characteristic of this algorithm is the small number of parameters required, so the mining process is easy for end-users. The algorithm proposed is compared with existing subgroup discovery evolutionary algorithms. The experimental results reveal the excellent behaviour of this algorithm, discovering comprehensible subgroups and behaving better than the other algorithms. The conclusions obtained were reinforced through a series of non-parametric tests. Adaptive Distance Metrics for Nearest Neighbour Classification based on Genetic Programming Alexandros Agapitos, Michael O'Neill, Anthony Brabazon Nearest Neighbour (NN) classification is a widely-used, effective method for both binary and multi-class problems. It relies on the assumption that class conditional probabilities are locally constant. However, this assumption becomes invalid in high dimensions, and severe bias can be introduced, which degrades the performance of the method. The employment of a locally adaptive distance metric becomes crucial in order to keep class conditional probabilities approximately uniform, whereby better classification performance can be attained. This paper presents a locally adaptive distance metric for NN classification based on a supervised learning algorithm (Genetic Programming) that learns a vector of feature weights for the features composing an instance query. Using a weighted Euclidean distance metric, this has the effect of adaptive neighbourhood shapes to query locations, stretching the neighbourhood along the directions for which the class conditional probabilities don't change much. Initial empirical results on a set of real-world classification datasets showed that the proposed method enhances the generalisation performance of standard NN algorithm, and that it is a competent method for pattern classification as compared to other learning algorithms.

22


EvoBIO Â programme Wednesday 3 April 1120-1300

Room 3

EvoBIO1: Gene Expression, Genetic Interactions and Regulatory Networks Chair: William S. Bush Multiple Threshold Spatially Uniform ReliefF for the Genetic Analysis of Complex Human Diseases Delaney Granizo-Mackenzie, Jason H. Moore Detecting genetic interactions without running an exhaustive search is a difficult problem. We present a new heuristic, multiSURF*, which can detect these interactions with high accuracy and in time linear in the number of genes. Our algorithm is an improvement over the SURF* algorithm, which detects genetic signals by comparing individuals close to, and far from, one another and noticing whether differences correlate with different disease statuses. Our improvement consistently outperforms SURF* while providing a large runtime decrease by examining only individuals very near and very far from one another. Additionally we perform an analysis on real data and show that our method provides new information. We conclude that multiSURF* is a better alternative to SURF* in both power and runtime. Dimensionality reduction via Isomap with lock-step and elastic measures for time series gene expression classification Carlotta Orsenigo, Carlo Vercellis Isometric feature mapping (Isomap) has proven high potential for nonlinear dimensionality reduction in a wide range of application domains. Isomap finds low-dimensional data projections by preserving global geometrical properties, which are expressed in terms of the Euclidean distances among points. In this paper we investigate the use of a recent variant of Isomap, called double-bounded tree-connected Isomap (dbt-Isomap), for dimensionality reduction in the context of time series gene expression classification. In order to deal with the projection of temporal sequences dbt-Isomap is combined with different lock-step and elastic measures which have been extensively proposed to evaluate time series similarity. These are represented by three Lp-norms, dynamic time warping and the distance based on the longest common subsequence model. Computational experiments concerning the classification of two time series gene expression data sets showed the usefulness of dbt-Isomap for dimensionality reduction. Moreover, they highlighted the effectiveness of L1-norm which appeared as the best alternative to the Euclidean metric for time series gene expression embedding. Supervising Random Forest Using Attribute Interaction Networks Qinxin Pan, Ting Hu, James D. Malley, Angeline S. Andrew, Margaret R. Karagas, Jason H. Moore Genome-wide association studies (GWAS) have become a powerful and affordable tool to study the genetic variations associated with common human diseases. However, only few of the loci found are associated with a moderate or large increase in disease risk and therefore using GWAS findings to study the underlying biological mechanisms remains a challenge. One possible cause for the "missing heritability" is the gene-gene interactions or epistasis. Several methods have been developed and among them Random Forest (RF) is a popular one. RF has been successfully applied in many studies. However, it is also known to rely on marginal main effects. Meanwhile, networks have become a popular approach for characterizing the space of pairwise interactions systematically, which can be informative for classification problems. In this study, we compared the findings of Mutual Information Network (MIN) to that of RF and observed that the variables identified by the two methods overlap with differences. To integrate advantages of MIN into RF, we proposed a hybrid algorithm, MIN-guided RF (MINGRF), which overlays the neighborhood structure of MIN onto the growth of trees. After comparing MINGRF to the standard RF on a bladder cancer dataset, we conclude that MINGRF produces trees with a better accuracy at a smaller computational cost.

23


EvoBIO Â programme Knowledge-constrained K-medoids Clustering of Regulatory Rare Alleles for Burden Tests R. Michael Sivley, Alexandra E. Fish, William S. Bush Rarely occurring genetic variants are hypothesized to influence human diseases, but statistically associating these rare variants to disease is challenging due to a lack of statistical power in most feasibly sized datasets. Several statistical tests have been developed to either collapse multiple rare variants from a genomic region into a single variable (presence/absence) or to tally the number of rare alleles within a region, relating the burden of rare alleles to disease risk. Both these approaches, however, rely on user-specification of a genomic region to generate these collapsed or burden variables, usually an entire gene. Recent studies indicate that most risk variants for common diseases are found within regulatory regions, not genes. To capture the effect of rare alleles within non-genic regulatory regions for burden tests, we contrast a simple sliding window approach with a knowledge-guided k-medoids clustering method to group rare variants into statistically powerful, biologically meaningful windows. We apply these methods to detect genomic regions that alter expression of nearby genes.

Wednesday 3 April 1430-1610

Room 3

EvoBIO2: Computational Methods and Evolution Chair: Mario Giacobini ACO-based Bayesian Network Ensembles for the Hierarchical Classification of AgeingRelated Proteins Khalid Salama, Alex Freitas The task of predicting protein functions using computational techniques is a major research area in the field of bioinformatics. Casting the task into a classification problem makes it challenging, since the classes (functions) to be predicted are hierarchically related, and a protein can have more than one function. One approach is to produce a set of local classifiers; each is responsible for discriminating between a subset of the classes in a certain level of the hierarchy. In this paper we tackle the hierarchical classification problem in a local fashion, by learning an ensemble of Bayesian network classifiers for each class in the hierarchy and combining their outputs with four alternative methods: a) selecting the best classifier, b) majority voting, c) weighted voting, and d) constructing a meta-classifier. The ensemble is built using ABC-Miner, our recently introduced Ant-based Bayesian Classification algorithm. We use different types of protein representations to learn different classification models. We empirically evaluate our proposed methods on an ageing-related protein dataset created for this research. Feature Selection and Classification of High Dimensional Mass Spectrometry Data: A Genetic Programming Approach Soha Ahmed, Mengjie Zhang, Lifeng Peng Biomarker discovery using mass spectrometry (MS) data is very useful in disease detection and drug discovery. The process of biomarker discovery in MS data must start with feature selection as the number of features in MS data is extremely large (e.g. thousands) while the number of samples is comparatively small. In this study, we propose the use of genetic programming (GP) for automatic feature selection and classification of MS data. This GP based approach works by using the features selected by two feature selection metrics, namely information gain (IG) and relief-f (REFS-F) in the terminal set. The feature selection performance of the proposed approach is examined and compared with IG and REFS-F alone on five MS data sets with different numbers of features and instances. Naive Bayes (NB), support vector machines (SVMs) and J48 decision trees (J48) are used in the experiments to evaluate the classification accuracy of the selected features. Meanwhile, GP is also used as a classification method in the experiments and its performance is compared with that of NB, SVMs and J48. The results show that GP as a feature selection method can select a smaller number of features with better classification performance than IG and REFS-F using NB, SVMs and J48. In addition, GP as a classification method also outperforms NB and J48 and achieves comparable or slightly better performance than SVMs on these data sets.

24


EvoBIO Â programme Structured populations and the maintenance of sex Peter A. Whigham, Grant Dick, Alden Wright, Hamish G. Spencer The maintenance of sexual populations has been an ongoing issue for evolutionary biologists, largely due to the two-fold cost of sexual versus asexual reproduction. Many explanations have been proposed to explain the benefits of sex, including the role of recombination in maintaining diversity and the elimination of detrimental mutations, the advantage of sex in rapidly changing environments, and the role of spatial structure, finite population size and drift. Many computational models have been developed to explore theories relating to sexual populations; this paper examines the role of spatial structure in supporting sexual populations, based on work originally published in 2006. We highlight flaws in the original model and develop a simpler, more plausible model that demonstrates the role of mutation, local competition and dispersal in maintaining sexual populations. Bloat free Genetic Programming: application to human oral bioavailability prediction (invited paper, as published in Int. J. Data Mining and Bioinformatics, Vol. 6, No. 6, 2012) Sara Silva, Leonardo Vanneschi Being able to predict the human oral bioavailability for a potential new drug is extremely important for the drug discovery process. This problem has been addressed by several prediction tools, with Genetic Programming providing some of the best results ever achieved. In this paper we use the newest developments of Genetic Programming, in particular the latest bloat control method, Operator Equalisation, to find out how much improvement we can achieve on this problem. We show examples of some actual solutions and discuss their quality, comparing them with previously published results. We identify some unexpected behaviours related to overfitting, and discuss the way for further improving the practical usage of the Genetic Programming approach

Wednesday 3 April 1630-1830 EvoBIO Posters Mining for Variability in the Coagulation Pathway: A Systems Biology Approach Davide Castaldi, Daniele Maccagnola, Daniela Mari, Francesco Archetti In this paper authors perform a variability analysis of a Stochastic Petri Net (SPN) model of the Tissue Factor induced coagulation cascade, one of the most complex biochemical networks. This pathway has been widely analyzed in literature mostly with ordinary differential equations, outlining the general behaviour but without pointing out the intrinsic variability of the system. The SPN formalism can introduce uncertainty to capture this variability and, through computer simulation allows to generate analyzable time series, over a broad range of conditions, to characterize the trend of the main system molecules. We provide a useful tool for the development and management of several observational studies, potentially customizable for each patient. The SPN has been simulated using Tau-Leaping Stochastic Simulation Algorithm, and in order to simulate a large number of models, to test different scenarios, we perform them using High Performance Computing. We analyze different settings for model representing the cases of healthy and different unhealthy subjects, comparing and testing their variability in order to gain valuable biological insights. Cell-based Metrics Improve the Detection of Gene-Gene Interactions using Multifactor Dimensionality Reduction Jonathan M. Fisher, Peter Andrews, Jeff Kiralis, Nicholas A. Sinnott-Armstrong, Jason H. Moore Multifactor Dimensionality Reduction (MDR) is a widely- used data-mining method for detecting and interpreting epistatic effects that do not display significant main effects. MDR produces a reduced- dimensionality representation of a dataset which classifies multi-locus genotypes into either high- or low-risk groups. The weighted fraction of cases and controls correctly labelled by this classification, the bal- anced accuracy, is typically used as a metric to select the best or most-fit model. We propose two new metrics for MDR to use in evaluating models, Variance and Fisher, and compare those metrics to two previously-used MDR metrics, Balanced Accuracy and Normalized Mutual Information. We find that the proposed metrics consistently outperform the existing metrics across a variety of scenarios.

25


EvoBIO programme Impact of Different Recombination Methods in a Mutation-Specific MOEA for a Biochemical Application Susanne Rosenthal, Nail El-Sourani, Markus Borschbach Peptides play a key role in the development of drug candidates and diagnostic interventions, respectively. The design of peptides is cost-intensive and difficult in general for several well-known reasons. Multi-objective evolutionary algorithms (MOEAs) introduce adequate in silico methods for finding optimal peptides sequences which optimizes several molecular properties. A mutation-specific fast non-dominated sorting GA (termed MSNSGA-II) was especially designed for this purpose. In this work, an empirical study is presented about the performance of MSNSGA-II which is extended by optionally three different recombination operators. The main idea is to gain an insight into the significance of recombination for the performance of MSNSGA-II in general - and to improve the performance with these intuitive recombination methods for biochemical optimization. The benchmark test for this study is a three-dimensional optimization problem, using fitness functions provided by the BioJava library. Optimal Use of Biological Expert Knowledge from Literature Mining in Ant Colony Optimization for Analysis of Epistasis in Human Disease Arvis Sulovari, Jeff Kiralis, Jason H. Moore The fast measurement of millions of sequence variations across the genome is possible with the current technology. As a result, a difficult challenge arise in bioinformatics: the identification of combinations of interacting DNA sequence variations predictive of common disease [1]. The Multifactor Dimensionality Reduction (MDR) method is capable of analysing such interactions but an exhaustive MDR search would require exponential time. Thus, we use the Ant Colony Optimization (ACO) as a stochastic wrapper. It has been shown by Greene et al. that this approach, if expert knowledge is incorporated, is effective for analysing large amounts of genetic variation[2]. In the ACO method integrated in the MDR package, a linear and an exponential probability distribution function can be used to weigh the expert knowledge. We generate our biological expert knowledge from a network of gene-gene interactions produced by a literature mining platform, Pathway Studio. We show that the linear distribution function is the most appropriate to weigh our scores when expert knowledge from literature mining is used. We find that ACO parameters significantly affect the power of the method and we suggest values for these parameters that can be used to optimize MDR in Genome Wide Association Studies that use biological expert knowledge. Emergence of motifs in model gene regulatory networks Marcin Zagórski Gene regulatory networks arise in all living cells, allowing the control of gene expression patterns. The study of their circuitry has revealed that certain subgraphs of interactions or motifs appear at anomalously high frequencies. We investigate here whether the overrepresentation of these motifs can be explained by the functional capabilities of these networks. Given a framework for describing regulatory interactions and dynamics, we consider in the space of all regulatory networks those that have a prescribed function. Markov Chain Monte Carlo sampling is then used to determine how these functional networks lead to specific motif statistics in the interaction structure. We conclude that different classes of network motifs are found depending on the functional constraint (multi-stability or oscillatory behaviour) imposed on the system evolution. The discussed computational framework can also be used for predicting regulatory interactions, if only the experimental gene expression pattern is provided. An Evolutionary Approach to Wetlands Design Marco Gaudesi, Andrea Marion, Tommaso Musner, Giovanni Squillero, Alberto Tonda Wetlands are artificial basins that exploit the capabilities of some species of plants to purify water from pollutants. The design process is currently long and laborious: such vegetated areas are inserted within the basin by trial and error, since there is no automatic system able to maximize the efficiency in terms of filtering. Only at the end of several attempts, experts are able to determine which is the most convenient configuration and choose up a layout. This paper proposes the use of an evolutionary algorithm to automate both the placement and the sizing of vegetated areas within a basin. The process begins from a random population of solutions and, evaluating their efficiency with

26


EvoBIO programme an state-of-the-art fluid-dynamics simulation framework, the evolutionary algorithm is able to automatically find optimized solution whose performance are comparable with those achieved by human experts. Improving the Performance of CGPANN for Breast Cancer Diagnosis using Crossover and Radial Basis Functions Timmy Manning, Paul Walsh Recently published evaluations of the topology and weight evolving artificial neural network algorithm Cartesian genetic programming evolved artificial neural networks (CGPANN) have suggested it as a potentially powerful tool for bioinformatics problems. In this paper we provide an overview of the CGPANN algorithm and a brief case study of its application to the Wisconsin breast cancer diagnosis problem. Following from this, we introduce and evaluate the use of RBF kernels and crossover to CGPANN as a means of increasing performance and consistency. A Multiobjective Proposal Based on the Firefly Algorithm for Inferring Phylogenies Sergio Santander-Jiménez, Miguel A. Vega-Rodríguez Recently, swarm intelligence algorithms have been applied successfully to a wide variety of optimization problems in Computational Biology. Phylogenetic inference represents one of the key research topics in this area. Throughout the years, controversy among biologists has arisen when dealing with this well-known problem, as different optimality criteria can give as a result discordant genealogical relationships. Current research efforts aim to apply multiobjective optimization techniques in order to infer phylogenies that represent a consensus between different principles. In this work, we apply a multiobjective swarm intelligence approach inspired by the behaviour of fireflies to tackle the phylogenetic inference problem according to two criteria: maximum parsimony and maximum likelihood. Experiments on four real nucleotide data sets show that this novel proposal can achieve promising results in comparison with other approaches from the state-of-theart in Phylogenetics. Hybrid Genetic Algorithms for Stress Recognition in Reading Nandita Sharma, Tom Gedeon Stress is a major problem facing our world today and affects everyday lives providing motivation to develop an objective understanding of stress during typical activities. Physiological and physical response signals showing symptoms for stress can be used to provide hundreds of features. This encounters the problem of selecting appropriate features for stress recognition from a set of features that may include irrelevant, redundant or corrupted features. In addition, there is also a problem for selecting an appropriate computational classification model with optimal parameters to capture general stress patterns. The aim of this paper is to determine whether stress can be detected from individual-independent computational classification models with a genetic algorithm (GA) optimization scheme from sensor sourced stress response signals induced by reading text. The GA was used to select stress features, select a type of classifier and optimize the classifierís parameters for stress recognition. The classification models used were artificial neural networks (ANNs) and support vector machines (SVMs). Stress recognition rates obtained from an ANN and a SVM without a GA were 68% and 67% respectively. With a GA hybrid, the stress recognition rate improved to 89%. The improvement shows that a GA has the capacity to select salient stress features and define an optimal classification model with optimized parameter settings for stress recognition.

27


EvoBIO programme Thurs 4 April 0930-1110

Room 3

EvoBio3 : Best Paper Candidates and Final Discussion Chair: Leonardo Vanneschi Time-point Specific Weighting Improves Coexpression Networks from Time-course Experiments (EvoBIO Best Paper Candidate) Jie Tan, Gavin Grant, Michael Whitfield, Casey Greene Integrative systems biology approaches build, evaluate, and combine data from thousands of diverse experiments. These strategies rely on methods that effectively identify and summarize gene-gene relationships within individual experiments. For gene-expression datasets, the Pearson correlation is often applied to build coexpression networks because it is both easily interpretable and quick to calculate. Here we develop and evaluate weighted Pearson correlation approaches that better summarize gene expression data into coexpression networks for synchronized cell cycle time-course experiments. These methods use experimental measurements of cell cycle synchrony to estimate appropriate weights through either sliding window or linear regression approaches. We show that these weights improve our ability to build coexpression networks capable of identifying phase-specific functional relationships between genes. We evaluate our method on diverse experiments and find that both weighted strategies outperform the traditional method. This weighted correlation approach is implemented in the Sleipnir library, an open source library used for integrative systems biology. Integrative approaches using properly weighted time-course experiments will provide a more detailed understanding of the processes studied in such experiments. Hybrid Multiobjective Artificial Bee Colony with Differential Evolution Applied to Motif Finding (EvoBIO Best Paper Candidate) David L. González-Álvarez, Miguel A. Vega-Rodríguez, Juan A. Gómez-Pulido, Juan M. Sánchez-Pérez The Multiobjective Artificial Bee Colony with Differential Evolution (MO-ABC/DE) is a new hybrid multiobjective evolutionary algorithm proposed for solving optimization problems. One important optimization problem in Bioinformatics is the Motif Discovery Problem (MDP), applied to the specific task of discovering DNA patterns (motifs) with biological significance, such as DNAprotein binding sites, replication origins or transcriptional DNA sequences. In this work, we apply the MO-ABC/DE algorithm for solving the MDP using as benchmark genomic data belonging to four organisms: drosophila melanogaster, homo sapiens, mus musculus, and saccharomyces cerevisiae. To demonstrate the good performance of our algorithm we have compared its results with those obtained by four multiobjective evolutionary algorithms, and their predictions with those made by thirteen well-known biological tools. As we will see, the proposed algorithm achieves good results from both computer science and biology point of views. Inferring Human Phenotype Networks from Genome-Wide Genetic Associations (EvoBIO Best Paper Candidate) Christian Darabos, Kinjal Desai, Richard Cowper-Sallari, Mario Giacobini, Britney E. Graham, Mathieu Lupien, Jason H. Moore Networks are commonly used to represent and analyze large and complex systems of interacting elements. We build a human phenotype network (HPN) of over 600 physical attributes, diseases, and behavioral traits; based on more than 6,000 genetic variants (SNPs) from Genome-Wide Association Studies data. Using phenotype-to-SNP associations, and HapMap project data, we link traits based on the common patterns of human genetic variations, expanding previous studies from a gene-centric approach to that of shared risk-variants. The resulting network has a heavily right-skewed degree distribution, placing it in the scale-free region of the network topologies spectrum. Additional network metrics hint that the HPN shares properties with social

28


EvoBIO Â programme

networks. Using a standard community detection algorithm, we construct phenotype modules of similar traits without applying expert biological knowledge. These modules can be assimilated to the disease classes. However, we are able to classify phenotypes according to shared biology, and not arbitrary disease classes. We present a collection of documented clinical connections supported by the network. Furthermore, we highlight phenotypes modules and links that may underlie yet undiscovered genetic interactions. Despite its simplicity and current limitations the HPN shows tremendous potential to become a useful tool both in the unveiling of the diseases' common biology, and in the elaboration of diagnosis and treatments. Final Discussion and Conclusion

EvoBIO Best Paper Awards The BioData Mining journal (edited by BioMed Central) has sponsored the EvoBIO best paper award for 2013 and will publish the best paper in their journal giving a full waiver of publication fees for the authors.. The two runner-up papers nominated for the best paper award will also be invited to expand their article for post-conference publication in the BioData Mining journal, with a 25% discount on publication fees. The winning paper will be announced at the EvoStar closing ceremony on Friday, 5 April at 1230.

www.biodatamining.org

29


EvoCOP programme Wednesday 3 April 1120-1300

Room 2

EvoCOP1 : Algorithmic Techniques Chairs: Christian Blum, Martin Middendorf An Analysis of Local Search for the Bi-objective Bidimensional Knapsack Problem Leonardo C. T. Bezerra, Manuel López-Ibáñez, Thomas Stützle Local search techniques are increasingly often used in multi-objective combinatorial optimization due to their ability to improve the performance of metaheuristics. The efficiency of multi-objective local search techniques heavily depends on factors such as (i) neighborhood operators, (ii) pivoting rules and (iii) bias towards good regions of the objective space. In this work, we conduct an extensive experimental campaign to analyze such factors in a Pareto local search (PLS) algorithm for the bi-objective bidimensional knapsack problem (bBKP). In the first set of experiments, we investigate PLS as a stand-alone algorithm, starting from random and greedy solutions. In the second set, we analyze PLS as a post-optimization procedure. A study of adaptive perturbation strategy for iterated local search Una Benlic, Jin-Kao Hao We investigate the contribution of a recently proposed adaptive diversification strategy (ADS) to the performance of an iterated local search (ILS) algorithm. ADS is used as a diversification mechanism by breakout local search (BLS), which is a new variant of the ILS metaheuristic. The proposed perturbation strategy adaptively selects between two types of perturbations (directed or random moves) of different intensities, depending on the current state of search. We experimentally evaluate the performance of ADS on the quadratic assignment problem (QAP) and the maximum clique problem (MAX-CLQ). Computational results accentuate the benefit of combining adaptively multiple perturbation types of different intensities. Moreover, we provide some guidance on when to introduce a weaker and when to introduce a stronger diversification into the search. The Generate-and-Solve Framework Revisited: Generating by Simulated Annealing Rommel Saraiva, Napoleão Nepomuceno, Plácido Pinheiro The Generate-and-Solve is a hybrid framework to cope with hard combinatorial optimization problems by artificially reducing the search space of solutions. In this framework, a metaheuristic engine works as a generator of reduced instances of the problem. These instances, in turn, can be more easily handled by an exact solver to provide a feasible (optimal) solution to the original problem. This approach has commonly employed genetic algorithms and it has been particularly effective in dealing with cutting and packing problems. In this paper, we present an instantiation of the framework for tackling the constrained two-dimensional non-guillotine cutting problem and the container loading problem using a simulated annealing generator. We conducted computational experiments on a set of difficult benchmark instances. Results show that the simulated annealing implementation overachieves previous versions of the Generate-and-Solve framework. In addition, the framework is shown to be competitive with current state-of-the-art approaches to solve the problems studied here. Solving Clique Covering in Very Large Sparse Random Graphs by a Technique Based on kFixed Coloring Tabu Search David Chalupa We propose a technique for solving the k-fixed variant of the clique covering problem (k-CCP), where the aim is to determine, whether a graph can be divided into at most k non-overlapping cliques. The approach is based on labeling of the vertices with k available labels and minimizing the number of non-adjacent pairs of vertices with the same label. This is an inverse strategy to k-fixed graph coloring, similar to a tabu search algorithm TabuCol. Thus, we call our method TabuCol-CCP. The technique allowed us to improve the best known results of specialized heuristics for CCP on very large sparse random graphs. Experiments also show a promise in scalability, since a large dense graph does not have to be stored. In addition, we show that Gamma-function, which is used to evaluate a solution from the neighborhood in graph coloring in O(1) time, can be used without modification to do the same in k-CCP. For sparse graphs, direct use of Gamma allows a significant decrease in space complexity of TabuCol-CCP to O(|E|), with recalculation of fitness possible with small overhead in O(log deg(v)) time, where deg(v) is the degree of the vertex, which is relabeled.

30


EvoCOP Â programme Wednesday 3 April 1430-1610

Room 2

EvoCOP2 : Applications Chair : Mario Ventresca Single Line Train Scheduling with ACO Marc Reimann, Jose Eugenio Leal In this paper we study a train scheduling problem on a single line that may be traversed in both directions by trains with different priorities travelling with different speeds. We propose an ACO approach to provide decision support for tackling this problem. Our results show the strong performance of ACO when compared to optimal solutions provided by CPLEX for small instances as well as to other heuristics on larger instances. Predicting Genetic Algorithm Performance on the Vehicle Routing Problem Using Information Theoretic Landscape Measures Mario Ventresca, Beatrice Ombuki-Berman, Andrew Runka In this paper we examine the predictability of genetic algorithm (GA) performance using information-theoretic fitness landscape measures. The outcome of a GA is largely based on the choice of search operator, problem representation and tunable parameters (crossover and mutation rates, etc). In particular, given a problem representation the choice of search operator will determine, along with the fitness function, the structure of the landscape that the GA will search upon. Statistical and information theoretic measures have been proposed that aim to quantify properties (ruggedness, smoothness, etc) of this landscape. In this paper we concentrate on the utility of information theoretic measures to predict algorithm output for various instances of the capacitated and time-windowed vehicle routing problem. Using a clustering-based approach we identify similar landscape structures within these problems and propose to compare GA results to these clusters using performance profiles. These results highlight the potential for predicting GA performance, and providing insight self-configurable search operator design. A Multiobjective Approach Based on the Law of Gravity and Mass Interactions for Optimizing Networks Alvaro Rubio-Largo, Miguel A. Vega-RodrĂ­guez In this work, we tackle a real-world telecommunication problem by using Evolutionary Computation and Multiobjective Optimization jointly. This problem is known in the literature as the Traffic Grooming problem and consists on multiplexing or grooming a set of low-speed traffic requests (Mbps) onto high-speed channels (Gbps) over an optical network with wavelength division multiplexing facility. We propose a multiobjective version of an algorithm based on the laws of motions and mass interactions (Gravitational Search Algorithm, GSA) for solving this NPhard optimization problem. After carrying out several comparisons with other approaches published in the literature for this optical problem, we can conclude that the multiobjective GSA (MO-GSA) is able to obtain very promising results. A Population-based Strategic Oscillation Algorithm for Linear Ordering Problem with Cumulative Costs Wei Xiao, Wenqing Chu, Zhipeng Lu, Tao Ye, Guang Liu, Shanshan Cui This paper presents a Population-based Strategic Oscillation (denoted by PBSO) algorithm for solving the linear ordering problem with cumulative costs (denoted by LOPCC). The proposed algorithm integrates several distinguished features, such as an adaptive strategic oscillation local search procedure and an effective population updating strategy. The proposed PBSO algorithm is compared with several state-of-the-art algorithms on a set of public instances up to 100 vertices, showing its efficacy in terms of both solution quality and efficiency. Moreover, several important ingredients of the PBSO algorithm are analyzed.

31


EvoCOP Â programme Wednesday 3 April 1630-1830 EvoCOP Poster Dynamic Evolutionary Membrane Algorithm in Dynamic Environments Chuang Liu, Min Han Several problems that we face in real word are dynamic in nature. For solving these problems, a novel dynamic evolutionary algorithm based on membrane computing is proposed. In this paper, the partitioning strategy is employed to divide the search space to improve the search efficiency of the algorithm. Furthermore, the four kinds of evolutionary rules are introduced to maintain the diversity of solutions found by the proposed algorithm. The performance of the proposed algorithm has been evaluated over the standard moving peaks benchmark. The simulation results indicate that the proposed algorithm is feasible and effective for solving dynamic optimization problems.

Thursday 4 April

0930-1110

Room 2

EvoCOP3 : Theory and Parallelization Automatic Algorithm Selection for the Quadratic Assignment Problem using Fitness Landscape Analysis Erik Pitzer, Andreas Beham, Michael Affenzeller In the last few years, fitness landscape analysis has seen an increase in interest due to the availability of large problem collections and research groups focusing on the development of a wide array of different optimization algorithms for diverse tasks. Instead of being able to rely on a single trusted method that is tuned and tweaked to the application more and more, new problems are investigated, where little or no experience has been collected. In an attempt to provide a more general criterion for algorithm and parameter selection other than ``it works better than something else we tried'', sophisticated problem analysis and classification schemes are employed. In this work, we combine several of these analysis methods and evaluate the suitability of fitness landscape analysis for the task of algorithm selection. Investigating Monte-Carlo Methods on the Weak Schur Problem Shalom Eliahou, Cyril Fonlupt, Jean Fromentin, Virginie Marion-Poty, Denis Robilliard, Fabien Teytaud Nested Monte-Carlo Search (NMC) and Nested Rollout Policy Adaptation (NRPA) are Monte-Carlo tree search algorithms that have proved their efficiency at solving one-player game problems, such as morpion solitaire or sudoku 16x16, showing that these heuristics could potentially be applied to constraint problems. In the field of Ramsey theory, the weak Schur number WS(k) is the largest integer n for which their exists a partition into k subsets of the integers [1,n] such that there is no x < y < z all in the same subset with x + y = z. Several studies have tackled the search for better lower bounds for the Weak Schur numbers WS(k), k <= 4. In this paper we investigate this problem using NMC and NRPA, and obtain a new lower bound for WS(6), namely WS(6) <= 582. A New Crossover for Solving Constraint Satisfaction Problems Reza Abbasian, Malek Mouhoub In this paper we investigate the applicability of Genetic Algorithms (GAs) for solving Constraint Satisfaction Problems (CSPs). Despite some success of GAs when tackling CSPs, they generally suffer from poor crossover operators. In order to overcome this limitation in practice, we propose a novel crossover specifically designed for solving CSPs. Together with a variable ordering heuristic and an integration into a parallel architecture, this proposed crossover enables the solving of large and hard problem instances as demonstrated by the experimental tests conducted on randomly generated CSPs based on the model RB. We will indeed demonstrate, through these tests, that our proposed method is superior to the known GA based techniques for CSPs. In addition, we will show that we are able to compete with the efficient MAC-based Abscon 109 solver for random problem instances. From Sequential to Parallel Local Search for SAT Alejandro Arbelaez, Philippe Codognet In the domain of propositional Satisfiability Problem (SAT), parallel portfolio-based algorithms have become a standard methodology for both complete and incomplete solvers. In this methodology several algorithms explore the search space in parallel, either independently or cooperatively with

32


EvoCOP Â programme some communication between the solvers. We conducted a study of the scalability of several SAT solvers in different application domains (crafted, verification, quasigroups and random instances) when drastically increasing the number of cores in the portfolio, up to 512 cores. Our experiments show that on different problem families the behaviors of different solvers vary greatly. We present an empirical study that suggests that the best sequential solver is not necessary the one with the overall best parallel speedup.

Thursday 4 April 1135-1315

Room 2

EvoCOP4 : Multi-objective Optimization Chair: Marc Schoenauer Adaptive MOEA/D for QoS-based web service composition Mihai Suciu, Denis Pallez, Marcel Cremene, Dumitru Dumitrescu QoS aware service composition is one of the main research problem related to \textit{Service Oriented Computing (SOC)}. A certain functionality may be offered by several services having different Quality of Service (QoS) attributes. Although the QoS optimization problem is multiobjective by its nature, most approaches are based on single-objective optimization. Compared to single-objective algorithms, multiobjective evolutionary algorithms have the main advantage that the user has the possibility to select a posteriori one of the Pareto optimal solutions. A major challenge that arises is the dynamic nature of the problem of composing web services. The algorithms performance is highly influenced by the parameter settings. Manual tuning of these parameters is not feasible. An evolutionary multiobjective algorithm based on decomposition for solving this problem is proposed. To address the dynamic nature of this problem we consider the hybridization between an adaptive heuristics and the multiobjective algorithm. The proposed approach outperforms state of the art algorithms. A Multi-Objective Feature Selection Approach Based on Binary PSO and Rough Set Theory Liam Cervante, Bing Xue, Lin Shang, Mengjie Zhang Feature selection has two main objectives of maximising the classification performance and minimising the number of features. However, most existing feature selection algorithms are single objective wrapper approaches. In this work, we propose a multi-objective filter feature selection algorithm based on binary particle swarm optimisation (PSO) and probabilistic rough set theory. The proposed algorithm is compared with other five feature selection methods, including three PSO based single objective methods and two traditional methods. Three classification algorithms naive bayes, decision trees and k-nearest neighbours) are used to test the generality of the proposed filter algorithm. Experiments have been conducted on six datasets of varying difficulty. Experimental results show that the proposed algorithm can automatically evolve a set of non-dominated feature subsets. In almost all cases, the proposed algorithm outperforms the other five algorithms in terms of both the number of features and the classification performance (evaluated by all the three classification algorithms). This paper presents the first study on using PSO and rough set theory for multi-objective feature selection. Multi-Objective AI Planning: Comparing Aggregation and Pareto Approaches Mostepha Redouane Khouadjia, Marc Schoenauer, Vincent Vidal, Johann DrĂŠo, Pierre SavĂŠant Most real-world Planning problems are multi-objective, trying to minimize both the makespan of the solution plan, and some cost of the actions involved in the plan. But most, if not all existing approaches are based on single-objective planners, and use an aggregation of the objectives to remain in the single-objective context. Divide-and-Evolve is an evolutionary planner that won the temporal deterministic satisficing track at the last International Planning Competitions (IPC). Like all Evolutionary Algorithms (EA), it can easily be turned into a Pareto-based Multi-Objective EA. It is however important to validate the resulting algorithm by comparing it with the aggregation approach: this is the goal of this paper. The comparative experiments on a recently proposed benchmark set that are reported here demonstrate the usefulness of going Paretobased in AI Planning.

33


EvoCOP programme Combinatorial Neighborhood Topology Particle Swarm Optimization Algorithm for the Vehicle Routing Problem Yannis Marinakis, Magdalene Marinaki One of the main problems in the application of a Particle Swarm Optimization in combinatorial optimization problems, especially in routing type problems like the Traveling Salesman Problem, the Vehicle Routing Problem, etc., is the fact that the basic equation of the Particle Swarm Optimization algorithm is suitable for continuous optimization problems and the transformation of this equation in the discrete space may cause loose of information and may simultaneously need a large number of iterations and the addition of a powerful local search algorithm in order to find an optimum solution. In this paper, we propose a different way to calculate the position of each particle which will not lead to any loose of information and will speed up the whole procedure. This was achieved by replacing the equation of positions with a novel procedure that includes a Path Relinking Strategy and a different correspondence of the velocities with the path that will follow each particle. The algorithm is used for the solution of the Capacitated Vehicle Routing Problem and is tested in the two classic set of benchmark instances from the literature with very good results.

Thursday 4 April

1430-1610 Room 2

EvoCOP5 : Hyperheuristics Chair : Günther Raidl A Hyper-heuristic with a Round Robin Neighbourhood Selection Ahmed Kheiri, Ender Özcan An iterative selection hyper-heuristic passes a solution through a heuristic selection process to decide on a heuristic to apply from a fixed set of low level heuristics and then a move acceptance process to accept or reject the newly created solution at each step. In this study, we introduce Robinhood hyper-heuristic whose heuristic selection component allocates equal share from the overall execution time for each low level heuristic, while the move acceptance component enables partial restarts when the search process stagnates. The proposed hyperheuristic is implemented as an extension to a public software used for benchmarking of hyperheuristics, namely HyFlex. The empirical results indicate that Robinhood hyper-heuristic is a simple, yet powerful and general multistage algorithm performing better than most of the previously proposed selection hyper-heuristics across six different Hyflex problem domains. Generalizing Hyper-heuristics via Apprenticeship Learning Shahriar Asta, Ender Özcan, Andrew J. Parkes, Şima Etaner-Uyar An apprenticeship-learning-based technique is used as a hyper-heuristic to generate heuristics for an online combinatorial problem. It observes and learns from the actions of a known-expert heuristic on small instances, but has the advantage of producing a general heuristic that works well on other larger instances. Specifically, we generate heuristic policies for online bin packing problem by using expert near-optimal policies produced by a hyper-heuristic on small instances, where learning is fast. The "expert" is a policy matrix that defines an index policy, and the apprenticeship learning is based on observation of the action of the expert policy together with a range of features of the bin being considered, and then applying a k-means classification. We show that the generated policy often performs better than the standard best-fit heuristic even when applied to instances much larger than the training set. Solving the Virtual Network Mapping Problem with Construction Heuristics, Local Search and Variable Neighborhood Descent Johannes Inführ, Günther R. Raidl The Virtual Network Mapping Problem arises in the context of Future Internet research. Multiple virtual networks with different characteristics are defined to suit specific applications. These virtual networks, with all of the resources they require, need to be realized in one physical network in a most cost effective way. Two properties make this problem challenging: Already finding any valid mapping of all virtual networks into the physical network without exceeding the

34


EvoCOP Â programme resource capacities is NP-hard, and the problem consists of two strongly dependent stages as the implementation of a virtual network's connections can only be decided once the locations of the virtual nodes in the physical network are fixed. In this work we introduce various construction heuristics, Local Search and Variable Neighborhood Descent approaches and perform an extensive computational study to evaluate the strengths and weaknesses of each proposed solution method.

Friday 5 April

0930-1110

Room 2

EvoCOP6 Best papers Chairs: Christian Blum, Martin Middendorf An Artificial Immune System based approach for solving the Nurse Re-Rostering Problem (EvoCOP Best Paper Candidate) Broos Maenhout, Mario Vanhoucke Personnel resources can introduce uncertainty in the operational processes. Constructed personnel rosters can be disrupted and render infeasible rosters. Feasibility has to be restored by adapting the original announced personnel rosters. In this paper, an Artificial Immune System for the nurse re-rostering problem is presented. The proposed algorithm uses problem-specific and even roster-specific mechanisms which are inspired on the vertebrate immune system. We observe the performance of the different algorithmic components and compare the proposed procedure with the existing literature. Balancing Bicycle Sharing Systems: A Variable Neighborhood Search Approach (EvoCOP Best Paper Candidate) Marian Rainer-Harbach, Petrina Papazek, Bin Hu, GĂźnther R. Raidl We consider the necessary redistribution of bicycles in public bicycle sharing systems in order to avoid rental stations to run empty or entirely full. For this purpose we propose a general Variable Neighborhood Search (VNS) with an embedded Variable Neighborhood Descent (VND) that exploits a series of neighborhood structures. While this metaheuristic generates candidate routes for vehicles to visit unbalanced rental stations, the numbers of bikes to be loaded or unloaded at each stop are efficiently derived by one of three alternative methods based on a greedy heuristic, a maximum flow calculation, and linear programming, respectively. Tests are performed on instances derived from real-world data and indicate that the VNS based on a greedy heuristic represents the best compromise for practice. In general the VNS yields good solutions and scales much better to larger instances than two mixed integer programming approaches. High-Order Sequence Entropies for Measuring Population Diversity in the Traveling Salesman Problem (EvoCOP Best Paper Candidate) Yuichi Nagata, Isao Ono We propose two entropy-based diversity measures for evaluating population diversity in a genetic algorithm (GA) applied to the traveling salesman problem (TSP). In contrast to a commonly used entropy-based diversity measure, the proposed ones take into account highorder dependencies between the elements of individuals in the population. More precisely, the proposed ones capture dependencies in the sequences of up to $m+1$ vertices included in the population (tours), whereas the commonly used one is the special case of the proposed ones with m=1. We demonstrate that the proposed entropy-based diversity measures with appropriate values of $m$ evaluate population diversity more appropriately than does the commonly used one.

35


EvoMusArt  programme Wednesday 3 April 1630-1830 EvoMUSART posters Darwinian Pianos: Realtime Composition based on Competitive Evolutionary Process Guido Kramann In this project a composition is achieved by two separate evolutionary algorithms (virtual pianists) executing and modifying a repetitive phrase in a cooperative manner - conversely this collaboration is directly counteracted by deliberate placement of a tone within the repetitive phrasing by one or other of the pianists. This action creates conflict and consequently it becomes a challenging task for the opposing pianist to introduce a similar change - thus the effect becomes combative and may be witnessed by an audience. The genetic representation for pitches is based on prime-number ratios and assigns lower Hamilton distances to more harmonically related frequency pairs. This and a special way to evaluate musical structure based on it seems to be correlated with good results in generated music pieces. Finally possibilities are discussed to bring "Darwinian Pianos" into musical practice. Swarmic Sketches and Attention Mechanism Mohammad Majid al-Rifaie, John Mark Bishop This paper introduces a novel approach deploying the mechanism of `attention' by adapting a swarm intelligence algorithm -- Stochastic Diffusion Search -- to selectively attend to detailed areas of a digital canvas. Once the attention of the swarm is drawn to a certain line within the canvas, the capability of another swarm intelligence algorithm -- Particle Swarm Intelligence -is used to produce a `swarmic sketch' of the attended line. The swarms move throughout the digital canvas in an attempt to satisfy their dynamic roles -- attention to areas with more details -- associated to them via their fitness function. Having associated the rendering process with the concepts of attention, the performance of the participating swarms creates a unique, nonidentical sketch each time the `artist' swarms embark on interpreting the input line drawings. The detailed investigation of the `creativity' of such systems have been explored in our previous work; nonetheless, this papers provides a brief account of the `computational creativity' of the work through two prerequisites of creativity within the swarm intelligence's two infamous phases of exploration and exploitation; these phases are described herein through the attention and tracing mechanisms respectively. Swarmic Paintings and Colour Attention Mohammad Majid al-Rifaie, Mark Bishop Swarm-based multi-agent systems have been deployed in non-photorealistic rendering for many years. This paper introduces a novel approach in adapting a swarm intelligence algorithm -- Stochastic Diffusion Search -- for producing non-photorealistic images. The swarmbased system is presented with a digital image and the agents move throughout the digital canvas in an attempt to satisfy the dynamic roles -- attention to different colours -- associated to them via their fitness function. Having associated the rendering process with the concepts of `attention' in general and colour attention in particular, this papers briefly discusses the `computational creativity' of the work through two prerequisites of creativity (i.e. freedom and constraints) within the swarm intelligence's two infamous phases of exploration and exploitation. Feature Selection and Novelty in Computational Aesthetics João Correia, Penousal Machado, Juan Romero, Adrian Carballal An approach for exploring novelty in expression-based evolutionary art systems is presented. The framework is composed of a feature extractor, a classifier, an evolutionary engine and a supervisor. The evolutionary engine exploits shortcomings of the classifier, generating misclassified instances. These instances update the training set and the classifier is re-trained. This iterative process forces the evolutionary algorithm to explore new paths leading to the creation of novel imagery. The experiments presented and analyzed herein explore different feature selection methods and indicate the validity of the approach.

36


EvoMusArt  programme Biologicallyâ&#x20AC;&#x201C;inspired Motion Pattern Design of Multiâ&#x20AC;&#x201C;legged Creatures Shihui Guo, Safa Tharib, Jian Chang, Jianjun Zhang In this paper, we propose a novel strategy to synthesize motion patterns for multi--legged creatures inspired by the biological knowledge. To prove the concept, our framework deploys an approach of coupling the dynamics model, the Inverted Pendulum Model, and the biological controller, the Central Pattern Generator, to synthesize the motion of multiple legged creatures. The dynamics model ensures the physical plausibility and allows the virtual character to react to the external perturbations, where the biological controller coordinates the motion of several legs with designed numerical operators, providing user-friendly high--level control. This novel framework is computational--efficient by taking advantages of the self-similarity in motion and able to animate characters with different skeletons.

Thursday 4 April 1135-1315 Room 4 EvoMUSART 1 - Interactivity and Applications Chair : James McDermott evoDrummer: Deriving rhythmic patterns through interactive genetic algorithms Maximos Kaliakatsos-Papakostas, Andreas Floros, Michael Vrahatis Drum rhythm automatic construction is an important step towards the design of systems which automatically compose music. This work describes a novel mechanism that allows a system, namely the evoDrummer, to create novel rhythms with reference to a base rhythm. The user interactively defines the amount of divergence between the base rhythm and the generated ones. The methodology followed towards this aim incorporates the utilization of Genetic Algorithms and allows the evoDrummer to provide several alternative rhythms with specific, controlled divergence from the selected base rhythm. To this end, the notion of rhythm divergence is also introduced, based on a set of 40 drum--specific features. Four population initialization schemes are discussed and an extensive experimental evaluation is provided. The obtained results demonstrate that, with proper population initialization, the evoDrummer is able to produce a great variety of rhythmic patterns which accurately encompass the desired divergence from the base rhythm. Application of an Island Model Genetic Algorithm for a Multi-Track Music Segmentation Problem Brigitte Rafael, Michael Affenzeller, Stefan Wagner Genetic algorithms have been introduced to the field of media segmentation including image, video, and also music segmentation since segmentation problems usually have complex search spaces. Music segmentation can give insight into the structure of a music composition so it is an important task in music information retrieval (MIR). Past approaches have applied genetic algorithms to achieve the segmentation of a single music track. However, music compositions usually contain multiple tracks so single track segmentations might miss important global structure information. This paper focuses on the introduction of an island model genetic algorithm to achieve single track segmentations with respect to the global structure of the composition. Story Characterization Using Interactive Evolution in a Multi-Agent System Malik Nairat, Palle Dahlstedt, Mats Nordahl We propose a character generative approach that integrates human creativity based on an agent-based system where characters are developed using interactive evolution. By observing their behaviour, the author can choose the characters that he likes during an interaction process. The evolved characters can then be used to build a story outline as a foundation for generating stories. This can provide storytelling authors with tools for the creation process of characters and stories.

37


EvoMusArt  programme Sentient World: Human-Based Procedural Cartography Antonios Liapis, Georgios Yannakakis, Julian Togelius This paper presents a first step towards a computer-aided design tool for the creation of game maps. The tool, named Sentient World, allows the designer to draw a rough terrain sketch, adding extra levels of detail through stochastic and gradient search. Novelty search generates a number of dissimilar artificial neural networks that are trained to approximate a designer's sketch and provide maps of higher resolution back to the designer. As the procedurally generated maps are presented to the designer (to accept, reject, or edit) the terrain sketches are iteratively refined into complete high resolution maps which may diverge from initial designer concepts. Results obtained on a number of test maps show that novelty search is beneficial for introducing divergent content to the designer without reducing the speed of iterative map refinement.

Thursday 4 April 1430-1610

Room 4

EvoMUSART 2 - Computational Aesthetics and Automation Chair: Juan Romero Finding Image Features Associated with High Aesthetic Value by Machine Learning Vic Ciesielski, Perry Barile, Karen Trist A major goal of evolutionary art is to get images of high aesthetic value. We assume that some features of images are associated with high aesthetic value and want to find them. We have taken two image databases that have been rated by humans, a photographic database and one of abstract images generated by evolutionary art software. We have computed 55 features for each database. We have extracted two categories of rankings, the lowest and the highest. Using feature extraction methods from machine learning we have identified the features most associated with differences. For the photographic images the key features are wavelet and texture features. For the abstract images the features are colour based features. Decision Chain Encoding: Evolutionary design optimization Patrick Janssen, Vignesh Kaushik A novel encoding technique is presented that allows constraints to be easily handled in an intuitive way. The proposed encoding technique structures the genotype-phenotype mapping process as a sequential chain of decision points, where each decision point consists of a choice between alternative options. In order to demonstrate the feasibility of the decision chain encoding technique, a case-study is presented for the evolutionary optimization of the architectural design for a large residential building. Art, Aesthetics, Evolution Jon McCormack This paper discusses issues in evolutionary art related to Art Theory and Aesthetics with a view to better understanding how they might contribute to both research and practice. Aesthetics is a term often used in evolutionary art, but is regularly used with conflicting or naive understandings. A selective history of evolutionary art as art is provided, with an examination of some art theories from within the field. A brief review of aesthetics as studied in philosophy and art theory follows. It is proposed that evolutionary art needs to resolve some important conflicts and be clearer about what it means by terms like ``art'' and ``aesthetics''. Finally some possibilities for how to resolve these conflicts are described. Evolving Glitch Art Eelco den Heijer In this paper we introduce Glitch art as a new representation in Evolutionary Art. Glitch art is a recent form of digital art, and can be considered an umbrella term for a variety of techniques that manipulate digital images by altering their digital encoding in unconventional ways. We gathered a number of basic glitch operations and created a `glitch recipe' which takes a source image (in a certain image format, like jpeg or gif) and applies one or more glitch operations. This glitch recipe is the genotype representation in our evolutionary GP art system. We present our glitch operations, the genotype, and the genetic operators initialisation, crossover and mutation. A glitch operation may `break' an image by destroying certain data in the image encoding, and

38


EvoMusArt programme therefore we have calculated the `fatality rate' of each glitch operation. A glitch operation may also result in an image that is visually the same as its original, and therefore we also calculated the visual impact of each glitch operation. Furthermore we performed an experiment with our Glitch art genotype in our unsupervised evolutionary art system, and show that the use of our new genotype results in a new class of images in the evolutionary art world.

Thursday 4 April

1630-1810

Room 4

EvoMUSART 3: Best Paper Nominees Chair: Penousal Machado EvoSpace-Interactive: A Framework to Develop Distributed Collaborative-Interactive Evolutionary Algorithms for Artistic Design (EvoMUSART Best Paper Candidate) Mario Garcia-Valdez, Leonardo Trujillo, Francisco Fernández de Vega, Juan Julián Merelo Guervós, Gustavo Olague Currently, a large number of computing systems and user applications are focused on distributed and collaborative models for heterogeneous devices, exploiting cloud-based approaches and social networking. However, such systems have not been fully exploited by the evolutionary computation community. This work is an attempt to bridge this gap, and integrate interactive evolutionary computation with a distributed cloud-based approach that integrates with social networking for collaborative design of artistic artifacts. Such an approach to evolutionary art could fully leverage the concept of memes as an idea that spreads from person to person, within a computational system. In particular, this work presents EvoSpace-Interactive, an open source framework for the development of collaborative-interactive evolutionary algorithms, a computational tool that facilitates the development of interactive algorithms for artistic design. A proof of concept application is developed on EvoSpace-Interactive called Shapes that incorporates the popular social network Facebook for the collaborative evolution of artistic images generated using the Processing programming language. Initial results are encouraging, Shapes illustrates that it is possible to use EvoSpace-Interactive to effectively develop and deploy a collaborative system. Inverse Mapping with Sensitivity Analysis for Partial Selection in Interactive Evolution (EvoMUSART Best Paper Candidate) Jonathan Eisenmann, Matthew Lewis, Rick Parent Evolutionary algorithms have shown themselves to be useful interactive design tools. However, current algorithms only receive feedback about candidate fitness at the whole-candidate level. In this paper we describe a model-free method, using sensitivity analysis, which allows designers to provide fitness feedback to the system at the component level. Any part of a candidate can be marked by the designer as interesting (i.e. having high fitness). This has the potential to improve the design experience in two ways: (1) The finer-grain guidance provided by partial selections facilitates more precise iteration on design ideas so the designer can maximize her energy and attention. (2) When steering the evolutionary system with more detailed feedback, the designer may discover greater feelings of satisfaction with and ownership over the final designs. Aesthetic Measures for Evolutionary Vase Design Kate Reed (EvoMUSART Best Paper Candidate) In order to avoid the expense of interactive evolution, some researchers have begun using aesthetic measures as fitness functions. This paper explores the potential of one of the earliest aesthetic measures by George Birkhoff as a fitness function in vase design after suitable modifications. Initial testing of vases of this form also revealed several other properties with a positive correlation with human-awarded scores. A suitable balance of these new measures along with Birkhoff's measure was found using feedback from volunteers, and vases evolved using the measure were also assessed for their aesthetic potential. Although the initial designs suffered from lack of diversity, some modifications led to a measure that enabled the evolution of a range of vases which were liked by many of the volunteers. The final range of vases included many shapes similar to those developed by human designers. Coupled with 3D printing techniques this measure allows automation of the whole process from conception to production. We hope that this demonstration of the theory will enable further work on other aesthetic products.

39


EvoCOMNET programme Wednesday 3 April 1630-1830

EvoCOMNET posters

Load Balancing in Distributed Applications Based on Extremal Optimization Ivanoe De Falco, Eryk Laskowski, Richard Olejnik, Umberto Scafuri, Ernesto Tarantino, Marek Tudruj The paper shows how to use Extremal Optimization in load balancing of distributed applications executed in clusters of multicore processors interconnected by a message passing network. Composed of iterative optimization phases which improve program task placement on processors, the proposed load balancing method discovers dynamically the candidates for migration with the use of an Extremal Optimization algorithm and a special quality model which takes into account the computation and communication parameters of the constituent parallel tasks. Assessed by experiments with simulated load balancing of distributed program graphs, a comparison of the proposed Extremal Optimization approach against a deterministic approach based on a similar load balancing theoretical model is provided. A Framework for Modeling Automatic Offloading of Mobile Applications Using Genetic Programming Gianluigi Folino, Francesco Sergio Pisani The limited battery life of the modern mobile devices is one of the key problems limiting their usage. The offloading of computation on cloud computing platforms can considerably extend the battery duration. However it is really hard not only to evaluate the cases in which the offloading guarantees real advantages on the basis of the requirements of application in terms of data transfer, computing power needed, etc., but also to evaluate if user requirements (i.e. the costs of using the clouds, a determined QoS required, etc.) are satisfied. To this aim, in this work it is presented a framework for generating models for taking automatic decisions on the offloading of mobile applications using a genetic programming (GP) approach. The GP system is designed using a taxonomy of the properties useful to the offloading process concerning the user, the network, the data and the application. Finally, the fitness adopted permits to give different weights to the four categories considered during the process of building the model.

Thursday 4 April

1135-1315

Room 3

EvoCOMNET 1 Chair: Antonio Della Cioppa An Evolutionary Framework for Routing Protocol Analysis in Wireless Sensor Networks Doina Bucur, Giovanni Iacca, Giovanni Squillero, Alberto Tonda Wireless Sensor Networks (WSNs) are widely adopted for applications ranging from surveillance to environmental monitoring. While powerful and relatively inexpensive, they are subject to behavioural faults which make them unreliable. Due to the complex interactions between network nodes, it is difficult to uncover faults in a WSN by resorting to formal techniques for verification and analysis, or to testing. This paper proposes an evolutionary framework to detect anomalous behaviour related to energy consumption in WSN routing protocols. Given a collection protocol, the framework creates candidate topologies and evaluates them through simulation on the basis of metrics measuring the radio activity on nodes. Experimental results using the standard Collection Tree Protocol show that the proposed approach is able to unveil topologies plagued by excessive energy depletion over one or more nodes, and thus could be used as an offline debugging tool to understand and correct the issues before network deployment and during the development of new protocols. Routing Low-Speed Traffic Requests onto High-Speed Lightpaths by Using a Multiobjective Firefly Algorithm Álvaro Rubio-Largo, Miguel A. Vega-Rodríguez Nowadays, the bandwidth requirements of the majority of traffic connection requests are in the range of Mbps. However, in optical networks each physical link is able to operate in the range of Gbps causing a huge waste of bandwidth as a result. Fortunately, using access station at each node of the optical network, several low-speed traffic requests may be multiplexed onto one high-speed channel. Multiplexing or grooming these low-speed requests is known in the literature as the Traffic Grooming problem - an NP-hard problem. Therefore, in this paper we propose the use of Evolutionary Computation for solving this telecommunication problem. The selected algorithm is an approach inspired by the flash pattern and characteristics of fireflies, the Firefly Algorithm (FA), but adapted to the multiobjective domain (MO-FA). After performing several experiments and comparing the results obtained by the MO-FA with those obtained by other approaches published in the literature, we can conclude that it is a good approach for solving this problem.

40


EvoCOMNET programme Pareto-optimal Glowworm Swarms Optimization for Smart Grids Management (EvoCOMNET Best Paper Candidate) Eleonora Riva Sanseverino, Maria Luisa Di Silvestre, Roberto Gallea This paper presents a novel nature-inspired multi-objective optimization algorithm. The method extends the glowworm swarm particles optimization algorithm with algorithmic enhancements which allow identifying optimal Pareto front in the objectives space. In addition, the system allows specifying constraining functions which are needed in practical applications. The framework has been applied to the power dispatch problem of distribution systems including Distributed Energy Resources (DER). Results for the test cases are reported and discussed elucidating both numerical and complexity analysis.

Thursday 4 April 1430-1610

Room 3

EvoCOMNET 2 Chair: Ivan De Falco Solving the Location Areas Scheme in Realistic Networks by Using a Multi-objective Algorithm Víctor Berrocal-Plaza, Miguel A. Vega-Rodríguez, Juan M. Sánchez-Pérez, Juan A. Gómez-Pulido The optimization of the management tasks in current mobile networks is an interesting research field due to the exponential increase in the number of mobile subscribers. In this paper, we study two of the most important management tasks of the Public Land Mobile Networks: the location update and the paging, since these two procedures are used by the mobile network to locate and track the Mobile Stations. There are several strategies to manage the location update and the paging, but we focus on the Location Areas scheme with a two-cycle sequential paging, a strategy widely applied in current mobile networks. This scheme can be formulated as a multi-objective optimization problem with two objective functions: minimize the number of location updates and minimize the number of paging messages. In previous works, this multi-objective problem was solved with single-objective optimization algorithms by means of the linear aggregation of the objective functions. In order to avoid the drawbacks related to the linear aggregation, we propose an adaptation of the Non-dominated Sorting Genetic Algorithm II to solve the Location Areas Planning Problem. Furthermore, with the aim of studying a realistic mobile network, we apply our algorithm to the SUMATRA network. Results show that our algorithm outperforms the algorithms proposed by other authors, as well as the advantages of a multi-objective approach. An Overlay Approach for Optimising Small-World Properties in VANETs Julien Schleich, Grégoire Danoy, Bernabé Dorronsoro, Pascal Bouvry Advantages of bringing small-world properties in mobile ad hoc networks (MANETs) in terms of quality of service has been studied and outlined in the past years. In this work, we focus on the specific class of vehicular ad hoc networks (VANETs) and propose to un-partition such networks and improve their small-world properties. To this end, a subset of nodes, called injection points, is chosen to provide backend connectivity and compose a fully-connected overlay network. The optimisation problem we consider is to find the minimal set of injection points to constitute the overlay that will optimise the small-world properties of the resulting network, i.e., (1) maximising the clustering coefficient (CC) so that it approaches the CC of a corresponding regular graph and (2) minimising the difference between the average path length (APL) of the considered graph and the APL of corresponding random graphs. In order to face this new multi-objective optimisation problem, the NSGAII algorithm was used on realistic instances in the city-centre of Luxembourg. The accurate tradeoff solutions found by NSGAII (assuming global knowledge of the network) will permit to better know and understand the problem. This will later ease the design of decentralised solutions to be used in real environments, as well as their future validation. Impact of the Number of Beacons in PSO-Based Auto-localization in UWB Networks EvoCOMNET Best Paper Candidate Stefania Monica, Gianluigi Ferrari In this paper, we focus on auto-localization of nodes in a static wireless network, under the assumption of known position of a few initial nodes, denoted as “beacons”. Assuming that Ultra Wide Band (UWB) signals are used for inter-node communications, we analyze the impact of the number of beacons on the location accuracy. Three different approaches to localization are considered, namely: the Two-Stage Maximum-Likelihood (TSML) method; the Plane Intersection (PI) method, and Particle Swarming Optimization (PSO). Simulation results show that PSO allows obtaining accurate position estimates with a small number of beacons, making it an attractive choice to implement effective localization algorithm.

41


EvoCOMPLEX programme Wednesday 3 April

1120-1300 room 4

EvoCOMPLEX 1 Chairs: Carlos Cotta, Robert Schaefer Multiobjective Evolutionary Strategy for Finding Neighbourhoods of Pareto-optimal Solutions Ewa Gajda-Zagórska In some cases of Multiobjective Optimization problems finding Pareto optimal solutions does not give enough knowledge about the shape of the landscape, especially with multimodal problems and non-connected Pareto fronts. In this paper we present a strategy which combines a hierarchic genetic algorithm consisting of multiple populations with rank selection. This strategy aims at finding neighbourhoods of solutions by recognizing regions with high density of individuals. We compare two variants of the presented strategy on a benchmark twocriteria minimization problem. Genetic Programming-Based Model Output Statistics for Short-Range Temperature Prediction Kisung Seo, Byeongyong Hyeon, Soohwan Hyun, Younghee Lee This paper introduces GP (Genetic Programming) based robust compensation technique for temperature prediction in short-range. MOS (Model Output Statistics) is a statistical technique that corrects the systematic errors of the model. Development of an efficient MOS is very important, but most of MOS are based on the idea of relating model forecasts to observations through a linear regression. Therefore it is hard to manage complex and irregular natures of the prediction. In order to solve the problem, a nonlinear and symbolic regression method using GP is suggested as the first attempt. The purpose of this study is to evaluate the accuracy of the estimation by GP based nonlinear MOS for the 3 days temperatures for Korean regions. This method is then compared to the UM model and shows superior results. The training period of summer in 2007-2009 is used, and the data of 2010 summer is adopted for verification. Evolutionary Multi-Agent System in Hard Benchmark Continuous Optimisation Sebastian Pisarski, Adam Rugała, Aleksander Byrski, Marek Kisiel-Dorohinicki It turns out that hybridizing agent-based paradigm with evolutionary computation brings a new quality to the field of meta-heuristics, enhancing individuals with possibilities of perception, interaction with other individuals (agents), adaptation of parameters, etc. In the paper such technique-an evolutionary multi-agent system (EMAS)-is compared with a classical evolutionary algorithm (Michalewicz model) implemented with allopatric speciation (island model). Both algorithms are applied to the problem of continuous optimisation in selected benchmark problems. The results are very promising, as agent-based computing turns out to be more effective than classical one, especially in difficult benchmark problems, such as highdimensional Rastrigin function.

42


EvoCOMPLEX programme Wednesday 3 April

1430-1610

Room 4

EvoCOMPLEX 2 Chairs: Carlos Cotta, Robert Schaefer The Small-World Phenomenon Applied to a Self-adaptive Resources Selection Model María Botón-Fernández, Francisco Prieto Castrillo, Miguel A. Vega-Rodríguez Small-world property is found in a wide range of natural, biological, social or transport networks. The main idea of this phenomenon is that seemingly distant nodes actually have very short path lengths due to the presence of a small number of shortcut edges running between clusters of nodes. In the present work, we apply this principle for solving the resources selection problem in grid computing environments (distributed systems composed by heterogeneous and geographically dispersed resources). The proposed model expects to find the most efficient resources for a particular grid application in a short number of steps. It also provides a self-adaptive ability for dealing with environmental changes. Finally, this selection model is tested in a real grid infrastructure. From the results obtained it is concluded that both a reduction in execution time and an increase in the successfully completed tasks rate are achieved. Partial Imitation Hinders Emergence of Cooperation in the Iterated Prisoner’s Dilemma with Direct Reciprocity Mathis Antony, Degang Wu, K.Y. Szeto The evolutionary time scales for various strategies in the iterated Prisoner's Dilemma on a fully connected network are investigated for players with finite memory, using two different kinds of imitation rules: the (commonly used) traditional imitation rule where the entire meta-strategy of the role model is copied, and the partial imitation rule where only the observed subset of moves is copied. If the players can memorize the last round of the game, a sufficiently large random initial population eventually reaches a cooperative equilibrium, even in an environment with bounded rationality (noise) and high temptation. With the traditional imitation rule the time scale to cooperation increases linearly with decreasing intensity of selection (or increasing noise) in the weak selection regime, whereas partial imitation results in an exponential dependence. Populations with finite lifetimes are therefore unlikely to ever reach a cooperative state in this setting. Instead, numerical experiments show the emergence and long persistence of a phase characterized by the dominance of always defecting strategies. A Memetic Approach to Bayesian Network Structure Learning Alberto Tonda, Evelyne Lutton, Giovanni Squillero, Pierre-Henri Wuillemin Bayesian networks are graphical statistical models that represent inference between data. For their effectiveness and versatility, they are widely adopted to represent knowledge in different domains. Several research lines address the NP-hard problem of Bayesian network structure learning starting from data: over the years, the machine learning community delivered effective heuristics, while different Evolutionary Algorithms have been devised to tackle this complex problem. This paper presents a Memetic Algorithm for Bayesian network structure learning, that combines the exploratory power of an Evolutionary Algorithm with the speed of local search. Experimental results show that the proposed approach is able to outperform state-ofthe-art heuristics on two well-studied benchmarks.

43


EvoENERGY programme Thursday 4 April

0930-1110

room 1

EvoENERGY 1 Chairs: Konrad Diwold, Kyrre Glette Domestic Load Scheduling Using Genetic Algorithms Ana Soares, Álvaro Gomes, Carlos Henggeler Antunes Hugo Cardoso An approach using a genetic algorithm to optimize the scheduling of domestic electric loads, according to technical and user-defined constraints and input signals, is presented and illustrative results are shown. The aim is minimizing the end-user’s electricity bill according to his/her preferences, while accounting for the quality of the energy services provided. The constraints include the contracted power level, end-users’ preferences concerning the admissible and/or preferable time periods for operation of each load, and the amount of available usable power in each period of time to account for variations in the (nonmanageable) base load. The load scheduling is done for the next 36 hours assuming that a dynamic pricing structure is known in advance. The results obtained present a noticeable decrease of the electricity bill when compared to a reference case in which there is no automated scheduling. Evolutionary Algorithm Based Control Policies for Flexible Optimal Power Flow over Time Stephan Hutterer, Michael Affenzeller, Franz Auinger General optimal power flow (OPF) is an important problem in the operation of electric power grids. Solution methods to the OPF have been studied extensively that mainly solve steadystate situations, ignoring uncertainties of state variables as well as their near-future. Thus, in a dynamic and uncertain power system, where the demand as well as the supply-side show volatile behavior, optimization methods are needed that provide solutions very quickly, eliminating issues on convergence speed or robustness of the optimization. This paper introduces a policy-based approach where optimal control policies are learned offline for a given power grid based on evolutionary computation, that later provide quick and accurate control actions in volatile situations. With such an approach, it's no more necessary to solve the OPF in each new situation by applying a certain optimization procedure, but the policies provide (near-) optimal actions very quickly, satisfying all constraints in a reliable and robust way. Thus, a method is available for flexible and optimized power grid operation over time. This will be essential for meeting the claims for the future of smart grids. Using a Genetic Algorithm for the Determination of Power Load Profiles Frédéric Krüger, Daniel Wagner, Pierre Collet Electrical distribution companies struggle to find precise estimations of energy demand for their networks. They have at their disposal statistical tools such as power load profiles, which are however usually not precise enough and do not take into account factors such as the presence of electrical heating devices or the type of housing of the end users. In this paper, we show how a genetic algorithm generated with the EASEA language can be successfully applied to solve a noisy blind source separation problem and create accurate power load profiles using real world data provided by Électricité de Strasbourg. The data includes load measurements of 20kV feeders as well as the energy consumption of more than 400,000 end users. The power load profiles obtained demonstrate considerable improvement in the estimation of load curves of 20kV feeders.

44


EvoENERGY Â programme Thursday 4 April

1135-1315

room 1

EvoENERGY 2 Chairs: Konrad Diwold, Kyrre Glette Comparing Ensemble-Based Forecasting Methods for Smart-Metering Data Oliver Flasch, Martina Friese, Katya Vladislavleva, Thomas Bartz-Beielstein, Olaf Mersmann, Boris Naujoks, JĂśrg Stork, Martin Zaefferer This work provides a preliminary study on applying state-of-the-art time-series forecasting methods to electrical energy consumption data recorded by smart metering equipment. We compare a custom-build commercial baseline method to modern ensemble-based methods from statistical time-series analysis and to a modern commercial GP system. Our preliminary results indicate that that modern ensemble-based methods, as well as GP, are an attractive alternative to custom-built approaches for electrical energy consumption forecasting. Evolving Non-Intrusive Load Monitoring Dominik Egarter, Anita Sobe, Wilfried Elmenreich Non-intrusive load monitoring (NILM) identifies used appliances in a total power load according to their individual load characteristics. In this paper we propose an evolutionary optimization algorithm to identify appliances, which are modeled as on/off appliances. We evaluate our proposed evolutionary optimization by simulation with Matlab, where we use a random total load and randomly generated power profiles to make a statement of the applicability of the evolutionary algorithm as optimization technique for NILM. Our results show that the evolutionary approach is feasible to be used in NILM systems and can reach satisfying detection probabilities.

EvoINDUSTRY Chair: Kevin Sim CodeMonkey; a GUI Driven Platform for Swift Synthesis of Evolutionary Algorithms in Java Reza Etemadi, Nawwaf Kharma, Peter Grogono CodeMonkey is a GUI driven software development platform that allows non-experts and experts alike to turn an evolutionary algorithm design into a working Java program, with a minimal amount of manual code entry. CodeMonkey allows for a great number of different configurations of the generic evolutionary scheme, and that allows users to apply it to many different applications. This paper describes the concepts behind CodeMonkey, its internal architecture and manner of use. It concludes with a simple application that exhibits its utilization for multi-dimensional function optimization. CodeMonkey is provided free of charge, for non-commercial users, as a plug-in for the Eclipse platform.

45


EvoFIN Â programme Wednesday 3 April

1120-1300

room 5

EvoFIN1 Chairs: Andrea Tettamanzi, Alexandros Agapitos On the Utility of Trading Criteria Based Retraining in Forex Markets Alexander Loginov, Malcolm I. Heywood This research investigates the ability of genetic programming (GP) to build profitable trading strategies for the Foreign Exchange Market (FX) of three major currency pairs (EURUSD, USDCHF and EURCHF) using one hour prices from 2008 to 2011. We recognize that such environments are likely to be non-stationary. Thus, we do not require a single training partition to capture all likely future behaviours. We address this by detecting poor trading behaviours and use this to trigger retraining. In addition the task of evolving good technical indicators (TI) and the rules for deploying trading actions is explicitly separated. Thus, separate GP populations are used to coevolve TI and trading behaviours under a mutualistic symbiotic association. The results of 100 simulations demonstrate that an adaptive retraining algorithm significantly outperforms a single-strategy approach (population evolved once) and generates profitable solutions with a high probability. Identifying Market Price Levels Using Differential Evolution Michael Mayo Evolutionary data mining is used in this paper to investigate the concept of support and resistance levels in financial markets. Specifically, Differential Evolution is used to learn support/resistance levels from price data. The presence of these levels is then tested in out-of-sample data. Our results from a set of experiments covering five years worth of daily data across nine different US markets show that there is statistical evidence for price levels in certain markets, and that Differential Evolution can uncover them. Evolving Hierarchical Temporal Memory-Based Trading Models Patrick Gabrielsson, Rikard KĂśnig, Ulf Johansson We explore the possibility of using the genetic algorithm to optimize trading models based on the Hierarchical Temporal Memory (HTM) machine learning technology. Technical indicators, derived from intraday tick data for the E-mini S&P 500 futures market (ES), were used as feature vectors to the HTM models. All models were configured as binary classifiers, using a simple buy-and-hold trading strategy, and followed a supervised training scheme. The data set was partitioned into multiple folds to enable a modified cross validation scheme. Artificial Neural Networks (ANNs) were used to benchmark HTM performance. The results show that the genetic algorithm succeeded in finding predictive models with good performance and generalization ability. The HTM models outperformed the neural network models on the chosen data set and both technologies yielded profitable results with above average accuracy.

46


EvoFIN programme Wednesday 3 April

1430-1610

room 5

EvoFIN2 Chairs: Andrea Tettamanzi, Alexandros Agapitos Robust Estimation of Vector Autoregression (VAR) Models Using Genetic Algorithms Ronald Hochreiter, Gerald Krottendorfer In this paper we present an implementation of a Vector autoregression (VAR) estimation model using Genetic Algorithms. The algorithm was implemented in R and compared to standard estimation models using least squares. A numerical example is presented to outline advantages of the GA approach. Usage Patterns of Trading Rules in Stock Market Trading Strategies Optimized with Evolutionary Methods Krzysztof Michalak, Patryk Filipiak, Piotr Lipiński This paper proposes an approach to analysis of usage patterns of trading rules in stock market trading strategies. Analyzed strategies generate trading decisions based on signals produced by trading rules. Weighted sets of trading rules are used with parameters optimized using evolutionary algorithms. A novel approach to trading rule pattern discovery, inspired by association rule mining methods, is proposed. In the experiments, patterns consisting of up to 5 trading rules were discovered which appear in more than 50% of trading experts optimized by evolutionary algorithm. Combining Technical Analysis and Grammatical Evolution in a Trading System Iván Contreras, J. Ignacio Hidalgo, Laura Núñez-Letamendia Trading systems are beneficial for financial investments due to the complexity of nowadays markets. Finance markets are influenced by a great amount of factors of different sources such as government policies, natural disasters, international trade, political factors etc. Secondly, traders, brokers or practitioners in general could be affected by human emotions, so their behaviour in the stock market becomes nonobjective. The high pressure induced by handling a large volume of money is the main reason of the so-called market psychology. Trading systems are able to avoid a great amount of these factors, allowing investors to abstract the complex flow of information and the emotions related to the investments. In this paper we compare two trading systems based on Evolutionary Computation. The first is a GA-based one and was already proposed and tested with Data from 2006. The second one is a grammatical evolution approach which uses a new evaluation method. Experimental results show that the later outperforms the GA approach with a set of selected companies of the Spanish market with 2012 Data.

47


EvoGAMES programme Wednesday 3 April 1630-1830 EvoGAMES posters Generating Artificial Neural Networks for Value Function Approximation in a Domain Requiring a Shifting Strategy Ransom K. Winder Artificial neural networks have been successfully used as approximating value functions for tasks involving decision making. In domains where a shift in judgment for decisions is necessary as the overall state changes, it is hypothesized here that multiple neural networks are likely to provide a benefit as an approximation of a value function over those that employ a single network. The card game Dominion was chosen as the domain to examine this, comparing neural networks generated by various machine learning methods successfully applied to other games (such as in TD-Gammon) to a genetic algorithm method for generating two neural networks for different phases of the game along with evolving the transition point. The results demonstrate a greater success ratio with the method hypothesized and suggest that future work examining more complex multiple neural network configurations could apply to this game domain as well as being applicable to other problems. Comparing Evolutionary Algorithms to Solve the Game of Mastermind Javier Maestro-Montojo, Juan Julián Merelo-Guervós, Sancho Salcedo-Sanz In this paper we propose a novel evolutionary approach to solve the Mastermind game, and compare the results obtained with that of existing algorithms. The new evolutionary approach consists of a hierarchical one involving two different evolutionary algorithms, one for searching the set of eligible codes, and the second one to choose the best code to be played at a given stage of the game. The comparison with existing algorithms provides interesting conclusions regarding the performance of the algorithms and how to improve it in the future. However, it is clear that Entropy is a better scoring strategy than Most Parts, at least for these sizes, being able to obtain better results, independently of the evolutionary algorithm.

Thursday 4 April 1630-1810

room 5

EvoGAMES 1 Chairs: Paolo Burrelli , JJ Merelo A Card Game Description Language Jose M. Font, Tobias Mahlmann, Daniel Manrique, Julian Togelius We present the first steps of developing a system capable of generating novel card games as well as computational analysis of existing games of the same genre. Towards this end, we present a formalisation of card game rules, and a context-free grammar Gcardgame capable of expressing the rules for a large variety of card games. Example derivations are given for Texas hold'em poker, blackjack and UNO. Random simulations are used both to verify the implementation of these well-known games, and to characterise the results of randomly deriving game rules from the grammar. In future work, this grammar will be used to evolve complete card games using a grammar-guided genetic program. Generating Map Sketches for Strategy Games Antonios Liapis, Georgios N. Yannakakis, Julian Togelius How can a human and an algorithm productively collaborate on generating game content? In this paper, we try to answer this question in the context of generating balanced and interesting lowresolution sketches for game levels. We introduce six important criteria for successful strategy game maps, and present map sketches optimized for one or more of these criteria via a constrained evolutionary algorithm. The sketch-based map representation and the computationally lightweight evaluation methods are geared towards the integration of the evolutionary algorithm within a mixed-initiative tool, allowing for the co-creation of game content by a human and an artificial designer.

48


EvoGAMES Â programme A Procedural Balanced Map Generator with Self-adaptive Complexity for the Real-Time Strategy Game Planet Wars RaĂşl Lara-Cabrera, Carlos Cotta, Antonio J. FernĂĄndez-Leiva Procedural content generation (PCG) is the programmatic generation of game content using a random or pseudo-random process that results in an unpredictable range of possible gameplay spaces. This methodology brings many advantages to game developers, such as reduced memory consumption. This works presents a procedural balanced map generator for a real-time strategy game: Planet Wars. This generator uses an evolutionary strategy for generating and evolving maps and a tournament system for evaluating the quality of these maps in terms of their balance. We have run several experiments obtaining a set of playable and balanced maps

Friday 5 April

0930-1110

room 5

EvoGAMES 2 Chairs: Paolo Burrelli , JJ Merelo Mechanic Miner: Reflection-Driven Game Mechanic Discovery and Level Design Michael Cook, Simon Colton, Azalea Raad, Jeremy Gow We introduce Mechanic Miner, an evolutionary system for discovering simple two-state game mechanics for puzzle platform games. We demonstrate how a reflection-driven generation technique can use a simulation of gameplay to select good mechanics, and how the simulationdriven process can be inverted to produce challenging levels specific to a generated mechanic. We give examples of levels and mechanics generated by the system, summarise a small pilot study conducted with example levels and mechanics, and point to further applications of the technique, including applications to automated game design. Report on game competitions 2013 Julian Togelius

49


EvoIASP programme Wednesday 3 April 1630-1830

EvoIASP posters

An Evolutionary Approach for Automatic Seedpoint Setting in Brain Fiber Tracking Tobias Pilic, Hendrik Richter In this paper we present an evolutionary approach for optimising the seedpoint setting in brain fiber tracking. Our aim is to use Diffusion Tensor Imaging (DTI) data and Diffusion Magnetic Resonance Imaging (dMRI) data for feeding an automatic fiber tracking approach. Our work focused on customising an evolutionary algorithm to find nerve fibers within diffusion data and allocate an appropriate number of seedpoints to them. This is necessary for the subsequent fiber reconstruction algorithms to work. The algorithm considerably enhances the speed and quality of the reconstruction and proves to be promising in leading to an automatic fiber tracking procedure used in medical imaging. Novel Initialisation and Updating Mechanisms in PSO for Feature Selection in Classification Bing Xue, Mengjie Zhang, Will N. Browne In classification, feature selection is an important, but difficult problem. Particle swarm optimisation (PSO) is an efficient evolutionary computation technique. However, the traditional personal best and global best updating mechanism in PSO limits its performance for feature selection and the potential of PSO for feature selection has not been fully investigated. This paper proposes a new initialisation strategy and a new personal best and global best updating mechanism in PSO to develop a novel feature selection algorithm with the goals of minimising the number of features, maximising the classification performance and simultaneously reducing the computational time. The proposed algorithm is compared with two traditional feature selection methods, a PSO based method with the goal of only maximising the classification performance, and a PSO based two-stage algorithm considering both the number of features and the classification performance. Experiments on eight benchmark datasets show that the proposed algorithm can automatically evolve a feature subset with a smaller number of features and higher classification performance than using all features. The proposed algorithm achieves significantly better classification performance than the two traditional methods. The proposed algorithm also outperforms the two PSO based feature selection algorithms in terms of the classification performance, the number of features and the computational cost. Prediction of Forest Aboveground Biomass: An Exercise on Avoiding Overfitting Sara Silva, Vijay Ingalalli, Susana Vinga, João M.B. Carreiras, Joana B. Melo, Mauro Castelli, Leonardo Vanneschi, Ivo Gonçalves, José Caldas Mapping and understanding the spatial distribution of forest aboveground biomass (AGB) is an important and challenging task. This paper describes an exercise of predicting the forest AGB of Guinea-Bissau, West Africa, using synthetic aperture radar data and measurements of tree size collected in field campaigns. Several methods were attempted, from linear regression to different variants and techniques of Genetic Programming (GP), including the cutting edge geometric semantic GP approach. The results were compared between each other in terms of root mean square error and correlation between predicted and expected values of AGB. None of the methods was able to produce a model that generalizes well to unseen data or significantly outperforms the model obtained by the state-of-the-art methodology, and the latter was also not better than a simple linear model. We conclude that the AGB prediction is a difficult problem, aggravated by the small size of the available data set.

50


EvoIASP Â programme Thursday 4 April

0930-1110

room 5

EvoIASP 1 Chairs: Stefano Cagnoni, Mengjie Zhang A Genetic Algorithm for Color Image Segmentation (IASP Best Paper Candidate) Alessia Amelio, Clara Pizzuti A genetic algorithm for color image segmentation is proposed. The method represents an image as a weighted undirected graph, where nodes correspond to pixels, and edges connect similar pixels. Similarity between two pixels is computed by taking into account not only brightness, but also color and texture content. Experiments on images from the Berkeley Image Segmentation Dataset show that the method is able to partition natural and human scenes in a number of regions consistent with human visual perception. A quantitative evaluation of the method compared with other approaches show that the genetic algorithm can be very competitive in partitioning color images. Adding Chaos to Differential Evolution for Range Image (IASP Best Paper Candidate) Ivanoe De Falco, Antonio Della Cioppa, Domenico Maisto, Umberto Scafuri, Ernesto Tarantino This paper presents a method for automatically pair-wise registering range images. Registration is effected adding chaos to a Differential Evolution technique and by applying the Grid Closest Point algorithm to find the best possible transformation of the second image causing 3D reconstruction of the original object. Experimental results show the capability of the method in picking up efficient transformations of images with respect to the classical Differential Evolution. The proposed method offers a good solution to build complete 3D models of objects from 3D scan datasets. Automatic Construction of Gaussian-Based Edge Detectors Using Genetic Programming (IASP Best Paper Candidate) Wenlong Fu, Mark Johnston, Mengjie Zhang Gaussian-based edge detectors have been developed for many years, but there are still problems with how to set scales for Gaussian filters and how to combine Gaussian filters. In order to address both problems, a Genetic Programming (GP) system is proposed to automatically choose scales for Gaussian filters and automatically combine Gaussian filters. In this study, the GP system is utilised to construct rotation invariant Gaussian-based edge detectors based on a benchmark image dataset. The experimental results show that the GP evolved Gaussian-based edge detectors are better than the Gaussian gradient and rotation invariant surround suppression to extract edge features.

51


EvoIASP Â programme Thursday 4 April 1135-1315

room 5

EvoIASP 2 Chairs: Stefano Cagnoni, Mengjie Zhang Implicit Fitness Sharing for Evolutionary Synthesis of License Plate Detectors Krzysztof Krawiec, Mateusz Nawrocki A genetic programming algorithm for synthesis of object detection systems is proposed and applied to the task of license plate recognition in uncontrolled lighting conditions. The method evolves solutions represented as data flows of high-level parametric image operators. In an extended variant, the algorithm employs implicit fitness sharing, which allows identifying the particularly difficult training examples and focusing the training process on them. The experiment, involving heterogeneous video sequences acquired in diverse conditions, demonstrates that implicit fitness sharing substantially improves the predictive performance of evolved detection systems, providing maximum recognition accuracy achievable for the considered setup and training data. Human Action Recognition from Multi-Sensor Stream Data by Genetic Programming Feng Xie, Andy Song, Vic Ciesielski This paper presents an approach to recognition of human actions such as sitting, standing, walking or running by analysing the data produced by the sensors of a smart phone. The data comes as streams of parallel time series from 21 sensors. We have used genetic programming to evolve detectors for a number of actions and compared the detection accuracy of the evolved detectors with detectors built from the classical machine learning methods including Decision Trees, Naive Bayes, Nearest Neighbour and Support Vector Machines. The evolved detectors were considerably more accurate. We conclude that the proposed GP method can capture complex interaction of variables in parallel time series without using predefined features. Genetic Programming for Automatic Construction of Variant Features in Edge Detection Wenlong Fu, Mark Johnston, Mengjie Zhang Basic features for edge detection, such as derivatives, can be further manipulated to improve detection performance. However, how to effectively combine different basic features remains an open issue and needs to be investigated. In this study, Genetic Programming (GP) is proposed to automatically and effectively construct rotation variant features based on basic features from derivatives, F-test, and histograms of images. To reduce computational cost in the training stage, the basic features only use the horizontal responses to construct new horizontal features. These new features are then combined with their own rotated versions in the vertical direction in the testing stage. The experimental results show that the rotation variant features constructed by GP combine advantages from the basic features, reduce drawbacks from basic features alone, and improve the detection performance.

52


EvoIASP Â programme Thursday 4 April

1430-1610

room 5

EvoIASP 3 Chair : Andy Song Land Cover/Land Use Multiclass Classification Using GP with Geometric Semantic Operators Mauro Castelli, Sara Silva, Leonardo Vanneschi, Ana Cabral, Maria J. Vasconcelos, LuĂ­s Catarino, JoĂŁo M.B. Carreiras Multiclass classification is a common requirement of many land cover/land use applications, one of the pillars of land science studies. Even though genetic programming has been applied with success to a large number of applications, it is not particularly suited for multiclass classification, thus limiting its use on such studies. In this paper we take a step forward towards filling this gap, investigating the performance of recently defined geometric semantic operators on two land cover/land use multiclass classification problems and also on a benchmark problem. Our results clearly indicate that genetic programming using the new geometric semantic operators outperforms standard genetic programming for all the studied problems, both on training and test data. Feedback-Based Image Retrieval Using Probabilistic Hypergraph Ranking Augmented by Ant Colony Algorithm Ling-Yan Pan, Yu-Bin Yang One fundamental issue in image retrieval is its lack of ability to take advantage of relationships among images and relevance feedback information. In this paper, we propose a novel feedback-based image retrieval technique using probabilistic hypergraph ranking augmented by ant colony algorithm, which aims at enhancing affinity between the related images by incorporating both semantic pheromone and low-level feature similarities. It can effectively integrate the high-order information of hypergraph and the feedback mechanism of ant colony algorithm. Extensive performance evaluations on two public datasets show that our new method significantly outperforms the traditional probabilistic hypergraph ranking on image retrieval tasks. Multiobjective Projection Pursuit for Semisupervised Feature Extraction Mihaela Elena Breaban The current paper presents a framework for linear feature extraction applicable in both unsupervised and supervised data analysis, as well as in their hybrid - the semi-supervised scenario. New features are extracted in a filter manner with a multi-modal genetic algorithm that optimizes simultaneously several projection indices. Experimental results show that the new algorithm is able to provide a compact and improved representation of the data set. The use of mixed labeled and unlabeled data under this scenario improves considerably the performance of constrained clustering algorithms such as constrained k-Means.

53


EvoINDUSTRY Â programme Wednesday 3 April 1630-1830 EvoINDUSTRY poster Multi-Objective Optimizations of Structural Parameter Determination for Serpentine Channel Heat Sink Xuekang Li, Xiaohong Hao, Yi Chen, Muhao Zhang, Bei Peng This paper presents an approach for modeling and optimization of the channel geometry of a serpentine channel heat sink using multi-objective genetic algorithm. A simple thermal resistance network model was developed to investigate the overall thermal performance of the serpentine channel heat sink. Based on a number of simulations, bend loss coefficient correlation for 1000<Re<2200 was obtained which was function of the aspect ratio (a), ratio of fins width to channel width (b). In this study, two objectives minimization of overall thermal resistance and pressure drop are carried out using multi-objective genetic algorithms. The channel width, fin width, channel height and inlet velocity are variables to be optimized subject to constraints of fixed length and width of heat sink. The study indicates that reduction in both thermal resistance and pressure drop can be achieved by optimizing the channel configuration and the inlet velocity.

Thursday 4 April

1135-1315

room 1

EvoINDUSTRY (with EvoENERGY 2) Chair: Kevin Sim CodeMonkey; a GUI Driven Platform for Swift Synthesis of Evolutionary Algorithms in Java Reza Etemadi, Nawwaf Kharma, Peter Grogono CodeMonkey is a GUI driven software development platform that allows non-experts and experts alike to turn an evolutionary algorithm design into a working Java program, with a minimal amount of manual code entry. CodeMonkey allows for a great number of different configurations of the generic evolutionary scheme, and that allows users to apply it to many different applications. This paper describes the concepts behind CodeMonkey, its internal architecture and manner of use. It concludes with a simple application that exhibits its utilization for multi-dimensional function optimization. CodeMonkey is provided free of charge, for non-commercial users, as a plug-in for the Eclipse platform.

54


EvoRISK Â programme Wednesday 3 April 1630-1830 EvoRISK poster The following people was not included in the EvoAPPS 2013 proceedings and will be published in the 2014 edition. An extended abstract is shown here:

Searching for Risk in Large Complex Spaces Kester Clegg, Rob Alexander University of York, York, U.K. {kester.clegg|rob.alexander}@york.ac.uk Keywords: Search, Risk, Safety, Air Traffic Control, Simulation, RAMS.

Extended Abstract: Safety analysts are starting to worry that large complex systems are becoming too difficult to analyze when part of the system is changed or placed under stress. Traditional safety analysis techniques may miss safety hazards or (more likely) some of the circumstances that can cause them. To help analysts discover hazards in complex systems, ASHiCS has created a proof-of-concept tool that uses evolutionary search and fast-time air traffic control (ATC) simulation to uncover airspace hazards. We use a fast-time ATC simulation (using RAMS Plus2) of an en-route air sector containing multiple flight paths and aircraft types, and into this we inject a serious incident (cabin pressure loss) that requires one aircraft to make an emergency descent. We then use a near-neighbor random hill-climber to search for high-risk variants of that situation: we run a wide range of variants, select the subset of variants that caused the most risk, and then mutate the aircraft entry times to create a new set of situation variants that will hopefully have even greater risk. Weighted heuristics are able to focus on specific events, flight paths or aircraft so that the search can effectively target incidents of interest. Air traffic is generated by specifying the characteristics of each aircraft entering the sector, namely aircraft type, aircraft entry time, its entry and exit flight level and the waypoints specifying its flight path and any level changes. The traffic input files are created using genetic algorithms with restrictions on the distribution of aircraft to predetermined flight paths and an enforcement of wake turbulence separation. Once the input files have been created, a non-graphic version of RAMS Plus (i.e. a version that runs without any visualization to speed up simulations) is executed and the outputs analyzed by heuristics in the ASHiCS software. The solution space is extremely large and cannot be exhaustively searched for the worst case; this is a problem for safety analysts who need a context to the search results so that they can determine event probabilities. Our initial approach has been to conduct a sensitivity analysis to try and discover more about the average fitness of the population during the evolutionary search. This provides some insight to the nature of the solution space, in terms of the frequency of other high risk scenarios and how sensitive such solutions are to mutation of their input configuration. Our initial results suggest that for very large solutions spaces, where high scoring solutions are relatively rare, the range of the mutation operator (i.e. the degree to which the mutation operator can change the original) has a significant effect on the average fitness of the population. From our experiments, mutation operators with large ranges that permit radical changes to the genotype perform significantly worse than operators with small ranges that permit gradual changes.

1

http://www.eurocontrol.int/eec/public/standard_page/WP_Fast_Time_Simulation_Tools.html

55


EvoRISK Â programme

Fig. 1. Sensitivity analysis. Mutation range = 300s

Fig. 2. Sensitivity analysis. Mutation range = 30s

56


EvoRISK programme Fig. 1 and 2 show the effect of altering the mutation operator by a factor of ten. Fitness score is plotted vertically, with the number of generations horizontally. The best scenario is passed on unchanged to the following generation (leading to plateaus where no mutation improves the previous best). The ten best of each generation are also plotted showing that as evolution proceeds, in the case of the large mutation operator, the distance between the best and the next ten best fitness increases, indicating that the mutations are largely destructive in nature. Conversely, when a small mutation operator is used, the average fitness of the leading scenarios increases as the best of each generation increases, and the gap between them is much narrower, indicating that a mutation operator with a small range (therefore less destructive) will perform much better for our type of solution space. In our paper we describe the evolutionary search used by ASHiCS to discover high risk configurations of air sector traffic. We provide arguments that show the use of destructive operators are unlikely to be effective in the type of high dimensional solution space represented by an air sector. The sensitivity analysis suggests that the solution landscape is composed of steep-sided, narrow peaks of high fitness, in which only very small mutations are likely to result in a fitness improvement. We believe this is an accurate characterization of the solution landscape, given that adjusting the start times of aircraft by just a few minutes can make a difference to conflict separation of several nautical miles. In our more recent work, we have increased the complexity of our scenarios by adding storms represented by a series of timed no-fly zones whose speed and direction are configured by the evolutionary search. We further extended our study into the nature of the solution landscape by providing detailed information of nearby variants of the final scenario discovered by the search using a two stage process (this research is still in progress). The information from the second stage should allow safety analysts to examine input parameter ranges of high risk variants, enabling them to better judge the probability of hazardous situations occurring in the sector being modeled, leading to more accurate recommendations for the implementation of safety barriers.

Bibliography 1. Koza, J., Keane, M., Streeter, M.: Evolving inventions. Scientific American, 52–59 (2003) 2. Fonlupt, C.: Book review: Genetic programming IV: Routine human competitive machine intelligence. Genetic Programming and Evolvable Machines 6, 231–233 (2005) 3. Alam, S., Zhao, W., Tang, J..: Discovering Delay Patterns in Arrival Traffic with Dynamic Continuous Descent Approaches using Co-Evolutionary Red Teaming. In : 9th ATM Seminar, Berlin (2011) 4. Alam, S., Lokan, C., Abbass, H.: What can make an airspace unsafe? characterizing collision risk using multiobjective optimization. In : IEEE Congress on Evolutionary Computation (CEC), 2012, pp.1-8 (2012) 5. White, D. R., Poulding, S.: A rigorous evaluation of crossover and mutation in genetic programming. In Vanneschi, L., Gustafson, S., A., D., Ebner, M., eds. : 12th European Conference, EuroGP 2009, Tübingen, Germany, pp.220-231 (2009) 6. Goldberg, D.: Genetic algorithms in search, optimization, and machine learning. Addison-Wesley (1989) 7. De Jong, K., Spears, W.: An analysis of the interacting roles of population size and crossover in genetic algorithms. In : Lecture Notes in Computer Science. Springer Berlin / Heidelberg (1991) 38-47 8. Lima, C., Goldberg, D., Sastry, K., Lobo, F.: Combining competent crossover and mutation operators: A probabilistic model building approach. In Beyer, H.-G., ed. : Proceedings of the 2005 conference on Genetic and evolutionary computation (GECCO '05), New York, pp.735-742 (2005) 9. Perrin, E., Kirwan, B., Stroup, R.: A Systemic model of ATM Safety: the integrated risk picture. In : 7th ATM Seminar, Barcelona (2007) 10. ARMS Working Group, 2007-2010: The ARMS Methodology for Operational Risk Assessment in Aviation Organisations. (v 4.1, March 2010) 11. Beyer, K., Goldstein, J., Ramakrishnan, R., Shaft, U.: When Is “Nearest Neighbor” Meaningful? In : Database Theory — ICDT’99: Lecture Notes in Computer Science. Springer, Berlin (1999) 217-235 12. Merz, P., Freisleben, B.: On the effectiveness of evolutionary search in high-dimensional NK-landscapes. In : Evolutionary Computation Proceedings, IEEE World Congress on Computational Intelligence, pp.741-745 (1998) 13. Anderson, D., Lin, X.: “A collision risk model for a crossing track separation methodology. Journal of Navigation 49(3), 337-349 (1996)

57


EvoNUM  /  EvoRISK  programme Thursday 4 April

1630-1810

room 2

EvoNUM & EvoRISK Chair: Anna Esparcia Repair Methods for Box Constraints Revisited Simon Wessing Box constraints are possibly the simplest kind of constraints one could think of in real-valued optimization, because it is trivial to detect and repair any violation of them. But so far, the topic has only received marginal attention in the literature compared to the more general formulations, although it is a frequent use case. It is experimentally shown here that different repair methods can have a huge impact on the optimizer's performance when using the covariance matrix selfadaptation evolution strategy (CMSA-ES). Also, two novel repair methods, specially designed for this algorithm, sometimes outperform the traditional ones. Towards Non-linear Constraint Estimation for Expensive Optimization Fabian Gieseke, Oliver Kramer Constraints can render a numerical optimization problem much more difficult to address. In many real-world optimization applications, however, such constraints are not explicitly given. Instead, one has access to some kind of a "black-box" that represents the (unknown) constraint function. Recently, we proposed a fast linear constraint estimator that was based on binary search. This paper extends these results by (a) providing an alternative scheme that resorts to the effective use of support vector machines and by (b) addressing the more general task of non-linear decision boundaries. In particular, we make use of active learning strategies from the field of machine learning to select reasonable training points for the recurrent application of the classifier. We compare both constraint estimation schemes on linear and non-linear constraint functions, and depict opportunities and pitfalls concerning the effective integration of such models into a global optimization process. Scalability of Population-Based Search Heuristics for Many-Objective Optimization Ramprasad Joshi, Bharat Deshpande Beginning with Talagrand's seminal work, isoperimetric inequalities have been used extensively in analysing randomized algorithms. We develop similar inequalities and apply them to analysing population-based randomized search heuristics for multiobjective optimization in Rn space. We demonstrate the utility of the framework in explaining an empirical observation so far not explained analytically: the curse of dimensionality, for many-objective problems. The framework makes use of the black-box model now popular in EC research. Malicious Automatically Generated Domain Name Detection Using Stateful-SBB Fariba Haddadi, H. Gunes Kayacik, A. Nur Zincir-Heywood, Malcolm I. Heywood Fariba Haddadi, A. Nur Zincir-Heywood, Malcolm I. Heywood, Gunes Kayacik This work investigates the detection of Botnet Command and Control (C&C) activity by monitoring Domain Name System (DNS) traffic. Detection signatures are automatically generated using evolutionary computation technique based on Stateful-SBB. The evaluation performed shows that the proposed system can work on raw variable length domain name strings with very high accuracy.

58


EvoPAR programme Wednesday 3 April 1630-1830 EvoPAR Poster Cloud Scale Distributed Evolutionary Strategies for High Dimensional Problems Dennis Wilson, Kalyan Veeramachaneni, Una-May O’Reilly We develop and evaluate a cloud scale distributed covariance matrix adaptation based evolutionary strategy for problems with dimensions as high as 400. We adopt an island based distribution model and rely on a peer-to-peer communication protocol. We identify a variety of parameters in a distributed island model that could be randomized leading to a new dynamic migration protocol that can prove advantageous when computing on the cloud. Our approach enables efficient and high quality distributed sampling while mitigating the latencies and failure risks associated with running on a cloud. We evaluate performance on a real world problem from the domain of wind energy: wind farm turbine layout optimization.

Thursday 4 April 0930-1110

room 4

EvoPAR Chairs: Francisco Fernández, J J Merelo On GPU Based Fitness Evaluation with Decoupled Training Partition Jazz Alyxzander Turner-Baggs Malcolm I. Heywood GPU acceleration of increasingly complex variants of evolutionary frameworks typically assumes that all the training data used during evolution resides on the GPU. Such an assumption places limits on the style of application to which evolutionary computation can be applied. Conversely, several coevolutionary frameworks explicitly decouple fitness evaluation from the size of the training partition. Thus, a subset of training exemplars is coevolved with the population of evolved individuals. In this work we articulate the design decisions necessary to support Pareto archiving for Genetic Programming under a commodity GPU platform. Benchmarking of corresponding CPU and GPU implementations demonstrates that the GPU platform is still capable of providing 20x reduction in computation time. EvoSpace: A Distributed Evolutionary Platform Based on the Tuple Space Model Mario García-Valdez, Leonardo Trujillo, Francisco Fernández de Vega, Juan Julián Merelo Guervós, Gustavo Olague This paper presents EvoSpace, a cloud service for the development of distributed evolutionary algorithms. EvoSpace is based on the tuplespace model, an associatively addressed memory space shared by several processes. Remote clients, here called EvoWorkers, connect to EvoSpace and periodically take a subset of individuals, perform evolutionary operations on them, and return a set of modified or new individuals. Several EvoWorkers carry out the evolutionary search in parallel and asynchronously, interacting with each other through the central repository. EvoSpace is designed to be domain independent and flexible, in the sense that in can be used with different types of evolutionary algorithms and applications. In this paper, two evolutionary algorithms are tested on the EvoSpace platform, a standard genetic algorithm benchmark and an interactive evolutionary system, achieving encouraging results. Cloud Driven Design of a Distributed Genetic Programming Platform Owen Derby, Kalyan Veeramachaneni, Una-May O’Reilly We describe how we design FlexGP, a distributed genetic programming (GP) system to efficiently run on the cloud. The system has a decentralized, fault-tolerant, cascading startup where nodes start to compute while more nodes are launched. It has a peer-to-peer neighbor discovery protocol which constructs a robust communication network across the nodes. Concurrent with neighbor discovery, each node launches a GP run differing in parameterization and training data from its neighbors. This factoring of parameters across learners produces many diverse models for use in ensemble learning.

59


EvoROBOT Â programme Wednesday 3 April 1630-1830 EvoROBOT Posters Virtual Spatiality in Agent Controllers: Encoding Compartmentalization JĂźrgen Stradner, Heiko Hamann, Christopher S.F. Schwarzer, Nico K. Michiels, Thomas Schmickl Applying methods of artificial evolution to synthesize robot controllers for complex tasks is still a challenging endeavor. We report an approach which might have the potential to improve the performance of evolutionary algorithms in the context of evolutionary robotics. We apply a controller concept that is inspired by signaling networks found in nature. The implementation of spatial features is based on Voronoi diagrams that describe a compartmentalization of the agent's inner body. These compartments establish a virtual embodiment, including sensors and actuators, and influence the dynamics of virtual hormones. We report results for an exploring task and an object discrimination task. These results indicate that the controller, that determines the principle hormone dynamics, can successfully be evolved in parallel with the compartmentalizations that determine the spatial features of the sensors, actuators, and hormones. Evolving Counter-Propagation Neuro-controllers for Multi-objective Robot Navigation Amiram Moshaiov, Michael Zadok This study follows a recent investigation on evolutionary training of counter-propagation neuralnetworks for multi-objective robot navigation in various environments. Here, in contrast to the original study, the training of the counter-propagation networks is done using an improved twophase algorithm to achieve tuned weights for both classification of inputs and the control function. The proposed improvement concerns the crossover operation among the networks, which requires special attention due to the classification layer. The numerical simulations, which are reported here, suggest that both the current and original algorithms are superior to the classical approach of using a feed-forward network. It is also observed that the current version has better convergence properties as compared with the original one. Toward Automatic Gait Generation for Quadruped Robots Using Cartesian Genetic Programming Kisung Seo, Soohwan Hyun This paper introduces a new gait generation method for quadruped robots using CGP (Cartesian Genetic Programming) based on refinement of regression polynomials for a joint trajectory. CGP uses as genotype a linear string of integers that are mapped to a directed graph. Therefore, some evolved modules for regression polynomials in CGP can be shared and reused among multiple outputs for joint trajectories. To investigate the effectiveness of the proposed approach, experiments on gaits were executed for a Bioloid quadruped robot in the Webots environment.

60


EvoROBOT programme Friday 5 April

0930-1110 room 3

EvoROBOT Chairs: Evert Haasdijk, Gusz Eiben Co-evolutionary Approach to Design of Robotic Gait Jan Černý, Jiří Kubalík Manual design of motion patterns for legged robots is difficult task often with suboptimal results. To automate this process variety of approaches have been tried including various evolutionary algorithms. In this work we present an algorithm capable of generating viable motion patterns for multi-legged robots. This algorithm consists of two evolutionary algorithms working in coevolution. The GP is evolving motion of a single leg while the GA deploys the motion to all legs of the robot. Proof-of-concept experiments show that the co-evolutionary approach delivers significantly better results than those evolved for the same robot with simple genetic programming algorithm alone. A Comparison between Different Encoding Strategies for Snake-Like Robot Controllers Dámaso Pérez-Moneo Suárez, Claudio Rossi In this paper, we present the results of the tests we have performed with different encoding strategies for evolving controllers for a snake-like robot. This study is aimed at finding the best encoding for on-line learning of basic skills, such as locomotion (both free and directed to an objective) and obstacle avoidance. The snake moves in a virtual world, which realistically simulates all the physical conditions of the real world. This is the first step of our research on online, embedded and open-ended evolution of robot controllers, where robots have to learn how to survive during their lifetime, and occasionally mate with other robots. A simple (1+1) evolutionary strategy has been adopted for lifetime learning. The results of the tests have shown that the best results, tested on the locomotion skills, is the “He1Sig” controller, that uses a different set of parameters for each segment of the snake but only one mutation rate, common to all parameters, that is encoded in the chromosome and therefore undergoes evolution itself. MONEE: Using Parental Investment to Combine Open-Ended and Task-Driven Evolution Nikita Noskov, Evert Haasdijk, Berend Weel, A.E. Eiben This paper is inspired by a vision of self-sufficient robot collectives that adapt autonomously to deal with their environment and to perform user-defined tasks at the same time. We introduce the MONEE algorithm as a method of combining open-ended (to deal with the environment) and task-driven (to satisfy user demands) adaptation of robot controllers through evolution. A number of experiments with simulated e-pucks serve as proof of concept and show that with MONEE, the robots adapt to cope with the environment and to perform multiple tasks. Our experiments indicate that MONEE distributes the tasks evenly over the robot collective without undue emphasis on easy tasks. Evolving Gaits for Physical Robots with the HyperNEAT Generative Encoding: The Benefits of Simulation Suchan Lee, Jason Yosinski, Kyrre Glette, Hod Lipson, Jeff Clune Creating gaits for physical robots is a longstanding and open challenge. Recently, the HyperNEAT generative encoding was shown to automatically discover a variety of gait regularities, producing fast, coordinated gaits, but only for simulated robots. A follow-up study found that HyperNEAT did not produce impressive gaits when they were evolved directly on a physical robot. A simpler encoding hand-tuned to produce regular gaits was tried on the same robot, and outperformed HyperNEAT, but these gaits were first evolved in simulation before being transferred to the robot. In this paper, we tested the hypothesis that the beneficial properties of HyperNEAT would outperform the simpler encoding if HyperNEAT gaits are first evolved in simulation before being transferred to reality. That hypothesis was confirmed, resulting in the fastest gaits yet observed for this robot, including those produced by nine different algorithms from three previous papers describing gait- generating techniques for this robot. This result is important because it confirms that the early promise shown by generative encodings, specifically HyperNEAT, are not limited to simulation, but work on challenging real-world engineering challenges such as evolving gaits for real robots.

61


EvoSTOC programme Friday 5 April

0930-1110

room 4

EvoSTOC Chair: Anabela Simões Adapting the Pheromone Evaporation Rate in Dynamic Routing Problems Michalis Mavrovouniotis, Shengxiang Yang Ant colony optimization (ACO) algorithms have proved to be able to adapt to dynamic optimization problems (DOPs) when stagnation behaviour is avoided. Several approaches have been integrated with ACO to improve its performance for DOPs. The adaptation capabilities of ACO rely on the pheromone evaporation mechanism, where the rate is usually fixed. Pheromone evaporation may eliminate pheromone trails that represent bad solutions from previous environments. In this paper, an adaptive scheme is proposed to vary the evaporation rate in different periods of the optimization process. The experimental results show that ACO with an adaptive pheromone evaporation rate achieves promising results, when compared with an ACO with a fixed pheromone evaporation rate, for different DOPs. Finding Robust Solutions to Dynamic Optimization Problems Haobo Fu, Bernhard Sendhoff, Ke Tang, Xin Yao Most research in evolutionary dynamic optimization is based on the assumption that the primary goal in solving Dynamic Optimization Problems (DOPs) is Tracking Moving Optimum (TMO). Yet, TMO is impractical in cases where keeping changing solutions in use is impossible. To solve DOPs more practically, a new formulation of DOPs was proposed recently, which is referred to as Robust Optimization Over Time (ROOT). In ROOT, the aim is to find solutions whose fitnesses are robust to future environmental changes. In this paper, we point out the inappropriateness of existing robustness definitions used in ROOT, and therefore propose two improved versions, namely survival time and average fitness. Two corresponding metrics are also developed, based on which survival time and average fitness are optimized respectively using population-based algorithms. Experimental results on benchmark problems demonstrate the advantages of our metrics over existing ones on robustness definitions survival time and average fitness. An Ant-Based Selection Hyper-heuristic for Dynamic Environments Berna Kiraz, A. Şima Etaner-Uyar, Ender Özcan Dynamic environment problems require adaptive solution methodologies which can deal with the changes in the environment during the solution process for a given problem. A selection hyper-heuristic manages a set of low level heuristics (operators) and decides which one to apply at each iterative step. Recent studies show that selection hyper-heuristic methodologies are indeed suitable for solving dynamic environment problems with their ability of tracking the change dynamics in a given environment. The choice function based selection hyper-heuristic is reported to be the best hyper-heuristic on a set of benchmark problems. In this study, we investigate the performance of a new learning hyper-heuristic and its variants which are inspired from the ant colony optimization algorithm components. The proposed hyper-heuristic maintains a matrix of pheromone intensities (utility values) between all pairs of low level heuristics. A heuristic is selected based on the utility values between the previously invoked heuristic and each heuristic from the set of low level heuristics. The ant-based hyper-heuristic performs better than the choice function and even its improved version across a variety of dynamic environments produced by the Moving Peaks Benchmark generator.

62


EuroPROJECTS Abstracts EuroPROJECTS:

EvoStar invited abstracts from European-funded projects where research is related to EvoStar’s thematic areas. These twelve projects will present posters on Wednesday, 3 April 16:30-18:30

ADVANCE

www.advance-logistics.eu Advanced predictive-analysis-based decision-support engine for logistics FP7-ICT-2009. 4.3 Intelligent Information Management

Partners : Computer and Automation Research Institute, Hungarian Academy of Sciences, Aston University, University of Groningen, Technology Transfer System srl, Palletways UK

Authors: Christopher Buckingham, Jan Chircop, Anikó Ekárt, Elisabeth Ilie-Zudor A typical hub-and-spoke logistics network accumulates a blizzard of data each month, generated every minute of every day by thousands of pallets travelling on hundreds of trailers for a million customers scattered across hundreds of thousands of postcodes, each with multiple different service requirements. Thousands of data items come on stream at any point of the network and need analysis to guide short-term decisions about lorry deployment (within minutes) as well as longer term plans for carrying capacity. Making sense of large data sets like these can be tackled from two directions. One is to exploit existing knowledge, often using humans who are experts in the domain. The alternative is to have no preconceptions and tackle the data from the bottom up, which is what computational models of ants and other social insects do. In ADVANCE, we are approaching the problem in both directions. A decision support system has been constructed based on how human decision makers use predictions of pallets numbers later in the day that are supplied by machine-learning regression methods. Regression makes assumptions about the structure of the data and it would be interesting to see how a purely bottom-up approach fares in comparison, which we are exploring with a new ant colony optimisation algorithm: the Multiple Pheromone Ant Clustering Algorithm (MPACA). Ant colony optimisation algorithms model the way ants use pheromones for marking paths to important locations in their environment. Pheromone traces are picked up, followed, and reinforced by other ants but also evaporate over time. Optimal paths attract more pheromone but less useful paths fade away. The main innovation of MPACA is its use of multiple pheromones, one for each value of all attributes describing objects in multidimensional space. For the ADVANCE project, we are investigating how the logistics prediction problem can be best configured for ants to use the current situation about consignments to predict likely end-of-day numbers. This is non-trivial because each time-point of the day has its own relationship with the endof-day number, which means there could be, in theory, an infinite number of models. Our regression models were limited to a set number of times of the day to reduce the number of models required. The ant approach will learn a single model and predictions will be determined by how ants congregate around the current situation (i.e a time-point earlier in the day). Each ant will be associated with an end-of-day number and the ants most attracted to the current situation will provide the prediction.

ASHICS

http://ashics.blogspot.co.uk/ Automating the Search for Hazards in Complex Systems SESAR WP-E Project E.02.05 Partners: University of York

Authors: Rob Alexander, Kester Clegg Safety analysts are starting to worry that large complex systems are becoming too difficult to analyze when part of the system is changed or placed under stress. Traditional safety analysis techniques may miss safety hazards or (more likely) some of the circumstances that can cause them. To help analysts discover hazards in complex systems, ASHiCS has created a proof-of-concept tool that uses evolutionary search and fast-time air traffic control (ATC) simulation to uncover airspace hazards.

63


EuroPROJECTS Abstracts We use a fast-time ATC simulation of an en-route sector containing multiple flight paths and aircraft types, and into this we inject a serious incident (cabin pressure loss) that requires one aircraft to make an emergency descent. To create extra workload for the air traffic controller (ATCo), we also introduce a storm moving across the sector. We then use a near-neighbour random hill-climber to search for high-risk variants of that situation: we run a wide range of variants, select the subset of variants that caused the most risk, and then mutate the aircraft entry times to create a new set of situation variants that will hopefully have even greater risk. The search space is extremely large and cannot be exhaustively searched for the worst case; this is a problem for safety analysts who need a context to the search results so that they can determine event probabilities. The approach we have taken is to provide a local context for the search results – for each high-risk situation found, we explore the space of situations that are very similar. This cannot demonstrate that the worst case scenario has been found, but it can indicate the expected frequency of that result in its near neighborhood. This provides some insight to the nature of the solution space within the near neighborhood of the original result, in terms of the frequency of high risk scenarios and how those scenarios differ from the original. The overall ASHiCS process produces a set of high-risk variant situations, which can then be studied in depth. This study can start in the original simulation, and then progress to higher-fidelity models and complementary analysis approaches. The contribution of ASHiCS is to identify the situation types that that generate the worst cases; analysts and can then investigate how to prevent that configuration of inputs leading to a hazard in the air sector being modeled.

AssisiBF

http://zool33.uni-graz.at/artlife/node/208 Animal and robot Societies Self-organise and Integrate by Social Interaction (bees and fish) FP7-ICT-2011.9.10 FET Proactive: Fundamentals of Collective Adaptive Systems (FOCAS) Partners: Universit´e Paris Diderot, CYBERTRONICA UG, ´erale de Lausann, Universidade de Lisboa

Sveuˇciliˇste u Zagrebu, Ecole Polytechnique F´ed

Authors: Thomas Schmickl, Ronald Thenius, Sibylle Hahshold, Martina Szopek, Karl Crailsheim ASSISI|bf is the name of an European Union funded project investigating novel methods for the development of bio-hybrid Collective Adaptive Systems (CASs), including artificial collective systems, as well as biological (eu-)social life forms. The main focus within this project is laid on experiments with two animal species: the honeybee Apis mellifera and the zebrafish Danio rerio. One aspect of the novel method to develop is to use autonomous reactive and learning coupled actuator-sensor units (CASUs), which allows to build self-adjusting, reactive networks of artificial agents, interacting with the biological eusocial live form. The CASUs can be seen as small immobile autonomous robots, equipped with a big variety of sensors and actuators, allowing to interact with the animals using only local physical cues. By programming selfadapting swarm algorithms into the CASUs, it is possible to generate two interacting social entities (animal and CASU), and to analyse the resulting behaviour. This will create a totally new perspective on the behaviour of social animals, as well as a new way to develop, tune and investigate Collective Adaptive Systems

BioBoost

www.bioboost.eu Biomass Based Energy Intermediates Boosting Biofuel Production FP7 ENERGY.2011.3.7-1 Development of new or improved sustainable bio-energy carriers

Partners: Karlsruher Institut fuer Technologie, Center for Research and Technology Hellas, CHIMAR Hellas AE, AVA-CO2-Forschung GmbH, EnBW Energie Baden-Württemberg AG, TNO, GRACE GmbH & Co.KG, IUNG, FHOÖ Forschungs & Entwicklungs GmbH, Neste Oil Corporation, SYNCOM Forschungs-und Entwicklungsberatung GmbH, DSM Chemical Technology R & D BV, Universitaet Stuttgart, Deutsches Zentrum fuer Luft- und Raumfahrt - DLR

Authors: Gabriel Kronberger, Erik Pitzer, Stephan Hutterer The general objective of this project is to pave the way for de-central conversion of (residual) biomass to energy carriers of higher density, being processable to transportation fuels, chemicals or heat and power at small-scale CHPs. De-central energy carrier production and central application are

64


EuroPROJECTS Abstracts embedded in a transregional (EU-wide) supply chain of biomass generation, transport, energy carrier and final product, which needs to be designed optimally for enabling economic feasibility. The consortium (coordinator: Karlsruhe Institute of Technology) consists of 14 interdisciplinary partners from all over the European Union. Simulation Optimization of a Multistage Biomass Network : Since the holistic transregional network of biomass supply, processing and utilization forms a complex as well as uncertain system, simulationbased optimization is applied for deriving optimal choices for number, technical type, size and location of facilities on the one hand, but for means and modes of transports between these facilities on the other hand. Therefore, a multi-stage problem is solved for combining longterm strategic network structure planning (facility layout) with short-term supply planning and scheduling (routing and inventory). Evolutionary Network Design: A mixed-integer optimization problem is being solved for finding optimal biomass networks with respect to both economic as well as ecologic objectives. Discrete variables describe placement decisions or routing strategies, while continuous variables are needed for deriving real-valued decisions on biomass utilization and processing quantities. Applying evolutionary algorithms for simulation optimization, special (mutation and crossover) operators are developed within HeuristicLab (http://dev.heuristiclab.com/) for treating complex solutions that represent holistic biomass scenarios, which finally get evaluated using simulation.

CoCoRo

http://cocoro.uni-graz.at Collective Cognitive Robots FP7-ICT-2009.2.1 Cognitive Systems and Robotics

Partners: University of Stuttgart, University of York, Université Libre de Bruxelles, Scuola Superiore Sant'Anna

Authors: Thomas Schmickl, Ronald Thenius, Payam Zahadat, Sibylle Hahshold, Christoph Möslinger, Karl Crailsheim The CoCoRo project aims at creating a swarm of interacting, cognitive, autonomous underwater robots. Since the start of the project in April 2011 the project has succeeded in creating the largest swarm of such underwater vehicles (AUVs). These AUVs are equipped with a set of sensors that allow them to interact and to perform tasks collectively, for example monitoring underwater environments as a distributed sensor network and collective underwater search. To perform these tasks the researchers are developing bio-inspired swarm algorithms e.g., based on slime-mold-inspired synchronization, firefly-inspired swarm-size measurement, fish-inspired shoaling algorithms, and novel types artificial neural network controllers. Before these algorithms are implemented on the real AUVs they are first tested in a simulator that has been developed specifically for the CoCoRo project and that allows the researchers to quickly test, evaluate, and adapt these novel controllers. The simulator also includes an evolutionary algorithm that can be used to speed up the search for optimal controller parameters complex swarm systems. The main contribution of this project to the field of bio-inspired computation is the expansion of the swarm robotics field into a new environment that has not been explored yet by a swarm of autonomous robots.

EPICS

www.epics-project.eu Engineering Proprioception in Computing Systems ICT-2009.8.5 Self-Awareness in Autonomic Systems

Partners: University of Paderborn, Imperial College London, University of Oslo, Klagenfurt University, University of Birmingham, EADS Innovation Works, ETH Zürich, AIT Austrian Institute of Technology

Author: Jim Torresen Proprioceptive systems are inherent systems in humans and animals which based on perceiving oneselves and the environment makes us able to naturally coordinate our motion and physical behavior without thinking about how to do it. The goal of this project is to develop foundational concepts and algorithms for proprioceptive systems to be applied in computing systems for selected applications.

65


EuroPROJECTS Abstracts One of the goals is to develop self-learning, self-organizing and adaptable systems through the application of nature-inspired computing and machine learning techniques. Exploiting self-awareness to anticipate changes in the environment is also included. The concepts are to be demonstrated within three widely different applications, including computational finance, distributed smart cameras and interactive mobile media systems. The concepts and foundations research in the project consists of developing abstract models and clear problems for study by: • designing algorithms for online learning in uncertain, dynamic, self-organising environments, e.g., bio-inspired, consensus, and gametheoretic techniques; bandit solvers, ensemble learning • developing mechanisms to ensure desirable global behaviour, e.g., market-based approaches • understanding the effect of interacting nodes’ objectives, strategies and behaviour on overall robustness, performance and quality of service. • exploit self-awareness in order to learn to anticipate changes in the environment

FoCAS

www.focas.eu Fundamentals of Collective Adaptive Systems: FoCAS Organisation, Coordination and Support FP7-ICT-2011.9.12 Coordination and support actions Partners: Edinburgh Napier University, Imperial College London, VU University Amsterdam, Università di Modena e Reggio Emilia, JKU Universitaet Linz

Author: Jennifer Willies The FOCAS Proactive Initiative funded by Future and Emerging Technologies at the EC aims to develop a foundational framework for collective adaptive systems with broad applicability. Our FoCAS project is the supporting FP7 Coordination Action which hopes to integrate, coordinate and help increase visibility for research carried out in the FOCAS Proactive Initiative and in other research fields related to collective adaptive systems. FoCAS also hopes to provide a positive interface between scientists and the science-aware public, showing how collective adaptive systems can impact on society. We started in March 2013 and over the next three years we intend to organise a series of workshops and summer schools, collect useful features and information for an online media lounge, encourage improved collaboration between researchers in Europe and internationally, and develop a roadmap indicating future research directions.. The FET FOCAS projects include • • • • • • •

ALLOW ENSEMBLES - ALLOW Ensembles ASSISI_bf - Animal and robot Societies Self-organise and Integrate by Social Interaction CASSTING - Collective Adaptive System SynThesIs with Non-zero-sum Games DIVERSIFY - Ecology-inspired software diversity for distributed adaptation in CAS SWARM-ORGAN - A theoretical framework for swarms of GRN-controlled agents which display adaptive tissue-like organisation UANTICOL - A Quantitative Approach to Management and Design of Collective and Adaptive Behaviours SMARTSOCIETY - Hybrid and Diversity-Aware Collective Adaptive Systems: When People Meet Machines to Build a Smarter Society

FoCAS membership is open and free so why not go to www.focas.eu and join us!

LOGICAL

www.project-logical.eu Transnational logistics improvement through cloud computing and innovative cooperative business models CENTRAL EUROPE Programme co-financed by the ERDF

Partners: Aufbauwerk Region Leipzig GmbH, Leipzig/Halle Airport, Logistics Network Leipzig-Halle, University of Leipzig, Wroclaw University of Economics, CL Consulting & Logistics Ltd, KIUT Regional Development Association, Bay Zoltan Foundation for Applied Research, Misdolin Plusz 2006 Kft, Interporto Bologna S.p.a., Province of Bologna, Regional Development Agency of Usti Region, PLC, Luka Koper, port and logistic system, d.d., Regional Development center Koper

Authors: Jerzy Korczak, Piotr Lipinski, Krzysztof Michalak, Patryk Filipiak, Paweł Sitek The LOGICAL project concerns the application of a novel computing paradigm – the cloud computing to the logistics sector. Challenges arising in this sector include just-in-time delivery, increasing

66


EuroPROJECTS Abstracts environmental costs of cargo transport and the interoperability of logistics businesses. Cloud computing technology has a potential to improve collaboration between logistics companies, especially between small logistics businesses and global players with their own IT systems. The main goal of the LOGICAL project is to enhance the interoperability of logistics businesses by providing a universal software environment based on the cloud computing technology. One of the important aspects of logistics interoperability that can be improved by using a unified computing platform is supply chain management. In the LOGICAL project, the SCM focuses on the 5th Party Logistics (5LP) model, which includes suppliers, manufacturers, distributors, retailers and customers. The proposed approach aims at multilevel cost optimization, i.e. optimizing the costs of production, transport, distribution and environmental protection, under a number of constraints, and taking into consideration timing, volume, capacity and various modes of transport. In the proposed approach a total cost defined as a sum of the following components: fixed cost, environmental cost, manufacturing cost and supply costs of manufacturers and distributors is minimized. Candidate solutions are represented as vectors of matrices where each matrix represents a single aspect of the economical model described above. Specimens need to satisfy a large set of constraints required by the model. Thus, a population of candidate solutions is split into two parts: a set of only feasible solutions and a set of only unfeasible ones. All feasible specimens and a certain group of infeasible ones with highest fitness are selected for reproduction at the beginning of each generation. A crossover method used in the proposed algorithm is based on exchanging random 1dimensional submatrices between each pair of complementary matrices in genotypes. Eventually, a new population is built up by first selecting a number of the best feasible individuals among parents and offspring and then adding infeasible ones with highest fitness. The evolutionary approach proposed in the project was validated in a number of computational experiments involving various supply chain management cases, which concern a few suppliers, manufacturers, distributors, retailers and customers, as well as a few modes of transport and a few types of products. Results confirm that the approach proposed was capable of finding quasi-optimal solutions, significant in practice and outperforming some popular benchmarks and standard solutions.

MIBISOC

www.softcomputing.es/mibisoc Medical Imaging Using Bio-inspired and Soft Computing FP7 PEOPLE-ITN-2008

Partners: European Centre for Soft Computing, Ghent University, Université Libre de Bruxelles, University of Nottingham, Università degli Studi di Parma, University of Granada, Henesis, Universitätsklinikum Freiburg

Author: Stefano Cagnoni MIBISOC is a Marie Curie Initial Training Network granted by the European Commission within the Seventh Framework Program (FP7 PEOPLE-ITN-2008). The main focus of MIBISOC is a training programme where sixteen early-stage researchers (ESRs) are being exposed to a wide variety of SC and BC techniques, as well as to the challenge of applying them to different situations and problems within the different MI stages. The general area of this project deals with the application of intelligent systems constituted by Bioinspired (BC) and Soft Computing (SC) techniques to real-world MI applications. Medical imaging (MI) is at the heart of many of today’s improved diagnostic and treatment technologies. Computer-based solutions are vastly more capable of both quantitative measurement of the medical condition and the pre-processing tasks of filtering, sharpening, and focusing image detail. Bio-inspired and Soft Computing techniques have been successfully applied in each of the fundamental steps of medical image processing and analysis (e.g. restoration, segmentation, registration or tracking). The natural partnership of humans and intelligent systems and machines in MI is to provide the clinician with powerful tools to take better decisions regarding diagnostic and treatment. This project aims to surpass the state of the art approaches applying intelligent systems constituted by SC-BC techniques to realworld MI applications. The partnership is composed of world-wide recognized researchers from 8 high quality scientific institutions (6 Universities, a R&D centres and a SME) that are involved as full partners, and 4 high

67


EuroPROJECTS Abstracts quality technical partners (a hospital, a SME, a Medical Company and a R&D centre) that will provide relevant industrial and medical experience to the ESRs. The collaboration of experts from the area of MI with those working on BC and SC applications to computer vision will generate new and viable methods and solutions from the combined ideas of these communities. The presence of both research and technical partners in the network, including hospitals and companies, provides the appropriate framework for application domain focused research. The trained ESRs are acquiring knowledge aimed at the development of intelligent systems based on BC-SC which are to provide flexible applicationoriented solutions to current MI in clinical and research problems.

MUSES

www.musesproject.eu Multiplatform Usable Endpoint Security FP7 ICT-2011.1.4 Trustworthy ICT

Partners: Universidad de Granada, Hamburger Informatik Technologie-Center, Université de Génève, CURE, WIND Telecomunicazioni, TXT e-solutions, KULeuven, Sweden Connectivity

Authors: Anna Esparcia, Miguel Juan, Sergio Zamarripa, JJ Merelo The overall purpose of MUSES is to foster corporate security by reducing the risks introduced by user behaviour. Data security and privacy are of fundamental importance to organizations, where they are defined and managed via Security Policies. Most security incidents are caused by organization insiders, either by their lack of knowledge or inadequate or malicious behaviour. Nowadays, information is highly distributed amongst corporate servers, the cloud and multiple personal devices like PDAs, tablets and smart phones. These are not only information holders but also user interfaces to access corporate information. Besides, the Bring Your Own Device practice is becoming more common in large organisations, posing new security threats and blurring the limits between corporate and personal use. In this situation enforcement of Security Policies is increasingly difficult, as any strategy with a chance to succeed must take into account several changing factors: information delocalisation, access from heterogeneous devices and mixing of personal and professional activities. Besides, any mechanism or control must be user friendly and provide non-intrusive, clear feedback on the risk being incurred at any time. MUSES will provide a device independent, user-centric and self-adaptive corporate security sys-tem, able to cope with the concept of seamless working experience on different devices, in which a user may start a session on a device and location and follow up the process on different devices and locations, without corporate digital asset loss. During project development, metrics of usability, context risk evaluation, user current trust situation and device exposure level will be defined and several guidelines for design of secure applications, company policies and context-based security requirements will be produced. A real-time trust and risk analysis engine will also be developed with security mechanisms hard to compromise once installed on the target platforms. Computational intelligence techniques will be used to develop a self-adaptive event correlation system that allows for the identification of risk patterns in real time. In particular we aim to: •Design of intelligent mechanisms for automated rule extraction. •Design of self learning and self adaptation mechanisms that are applicable to all the tools developed in order to ensure that they automatically adapt to the current risk level, the user, the location and the platform/device employed, while at the same time preserving the users' privacy

NASCENCE

http://nascenceproject.blogspot.co.uk/ NAnoSCale Engineering for Novel Computation using Evolution FP7 ICT-2011.9.6 Unconventional Computation (UCOMP)

Partners: University of Twente, University of Durham, University of York, Norwegian University of Scienceand Technology, SUPSI-IDSIA, University of York

Authors: Hajo Broersma, Mike Petty, Gunnar Tufte, Jürgen Schmidhuber, Julian F. Miller The aim of this project is to model, understand and exploit the behaviour of evolving nanosystems (e.g. networks of nanoparticles, carbon nanotubes or films of graphene) with the long term goal to build

68


EuroPROJECTS Abstracts information processing devices exploiting these architectures without reproducing individual components. With an interface to a conventional digital computer we will use computer controlled manipulation of physical systems to evolve them towards doing useful computation. During the project our target is to lay the technological and theoretical foundations for this new kind of information processing technology, inspired by the success of natural evolution and the advancement of nanotechnology, and the expectation that we will soon reach the limits of miniaturisation in digital circuitry (Moore's Law). The mathematical modelling of the configuration of networks of nanoscale particles combined with the embodied realisation of such systems through computer controlled stochastic search can strengthen the theoretical foundations of the field while keeping a strong focus on their potential application in future devices. Members of the consortium have already demonstrated proof of principle by the evolution of liquid crystal computational processors for simple tasks, but these earlier studies have only scraped the surface of what such systems may be capable of achieving. With this project we want to develop alternative approaches for situations or problems that are challenging or impossible to solve with conventional methods and models of computation. Achieving our objectives fully would provide not only a major disruptive technology for the electronics industry but probably the foundations of the next industrial revolution. Overall, we consider that this is to be a highly adventurous, high risk project with an enormous potential impact on society and the quality of life in general, including medicine, everyday household items, energy-saving policies, security, and communication.

SYMBRION

www.symbrion.eu Symbiotic evolutionary robot organisms FP7 ICT-2007.8.2 Pervasive adaptation Partners: Universität Stuttgart, Universität Graz, Vrije Universiteit, Universität Karlsruhe. Flanders Institute for Biotechnology, University of the West of England, Bristol, Eberhard Karls Universität Tübingen, University of York, Université Libre de Bruxelles, Institut National de Recherche en Informatique et Automatique

Authors: Marc Schoenauer, Jon Timmis The focus of this project is to investigate and develop novel principles of adaptation and evolution for symbiotic multi-robot organisms based on a variety of bio-inspired approaches. Such robot organisms will consist of super-large-scale swarms of robots, which can dock with each other and symbiotically share energy and computational resources within a single artificial organism. When it is advantageous to do so, these swarm robots can dynamically aggregate into one or many symbiotic organisms and collectively interact with the physical world via a variety of sensors and actuators. The project has developed novel evolutionary approaches to locomotion of artificial organisms, evolutionary approaches to learning, and the collective behaviour of swarms, using implicit fitness functions. We have developed novel morphogenesis approaches to the construction of such organisms and abilities to self-repair. Such approaches will lead to adaptive, evolvable and scalable robotic systems. The extraordinary potential and capability of autonomous large-scale symbiotic self-aggregation, reprogramming and evolution would open-up a wide range of current and future applications. The main application scenarios of such artificial organisms would be human-free environments with a high degree of danger or uncertainty as e.g. hazardous or space environments. The consortium represents leading organizations in different research fields and provides the critical mass of expertise and resources.

69


EvoTRANSFER Thursday 4 April 1630-1810

Room 3

EvoTRANSFER : Matching Technology Providers and Technology Users Chair: Andrea Tettamanzi This technology-transfer event showcases some technology solutions that the evolutionary computation community can offer to industry. Five short presentations will be made, followed by some industrial participants illustrating the problems they are facing as technology users, and for which they are seeking solutions. An open discussion session will follow. Technology Provider Presentations by Ami Moshaiov “Supporting concept selection for design and planning by evolutionary multi-objective optimization” The conceptual design stage is a crucial design step. We introduced a novel methodology to support concept selection, which we term Set-Based Concept (SBC) approach. The SBC approach constitutes a revolution to design space exploration and concept selection. According to this approach design space exploration is simultaneously done at two levels including the conceptual solution level and the detailed solution level. The presentation will include an introduction to the SBC approach and a discussion on its variants, and in particular those that have been developed at Tel-Aviv University. Currently we collaborate with the aircraft industry to implement our tools to a conceptual design problem of interest to that industry. Here, we propose to collaborate with other industries to check the potential of our tools to support concept selection by the industrial partner. Contact: moshaiov@post.tau.ac.il Gabriel Kronberger “Applications of Evolved Virtual Sensors in the Automotive Industry” We will show two concrete successful applications of genetic programming to evolve virtual sensors. In the first application we used a symbolic regression approach to evolve virtual sensors for NOx and soot emissions of diesel engines based on data from an engine test bench. The evolved virtual sensors are highly accurate and compact and can be used to estimate emissions based solely on easily measureable engine data (e.g. RPM, fuel consumption, temperatures). In the second application we used the same approach to evolve virtual sensors for the blast furnace process for the production of molten iron. The resulting models accurately model the unobservable internal state of the blast furnace and can be used to improve the control and stability of the process. Contact: gabriel.kronberger@fh-hagenberg.at Andreas Beham “Pick-optimized Storage Assignment in Production Warehouses” Picking and routing are two time-consuming processes in warehouse operations. We have taken a look on how to optimize the location of items in the warehouse of one of our industry partners in the automotive industry. In the presentation we will give an overview of the implemented approach, the software solution, and some of the lessons learnt. Contact: andreas.beham@fh-hagenberg.at Stephan Hutterer “Smart Electric Grid Engineering with HeuristicLab” Soft computing techniques become of increasing importance for future power systems, with manifold applications ranging from real-time generation scheduling over power flow control to processing of huge demand or supply data. HeuristicLab provides a generic workbench comprising soft computing techniques, suitable to be used for typical power grid engineering problems. Different show cases shall

70


EvoTRANSFER demonstrate the application to data mining issues, where accurate forecasting models are built for customer demand prognosis or renewable plants' generation prediction based on measurement data. A second application area illustrates the utilization of HeuristicLab for optimization of smart grid control and planning tasks. Contact: stephan.hutterer@fh-wels.at John Woodward â&#x20AC;&#x153;DAASE (Dynamic Adaptive Automated Software Engineering)â&#x20AC;? http://daase.cs.ucl.ac.uk/ Current software development processes are expensive, laborious and error prone. They achieve adaptivity at only a glacial pace, largely through enormous human effort, forcing highly skilled engineers to waste significant time adapting many tedious implementation details. Often, the resulting software is equally inflexible, forcing users to also rely on their innate human adaptivity to find "workarounds". Yet software is one of the most inherently flexible engineering materials with which we have worked, DAASE seeks to use computational search as an overall approach to achieve the software's full potential for flexibility and adaptivity. In so-doing we will be creating new ways to develop and deploy software. This is the new approach to software engineering DAASE seeks to create. It places computational search at the heart of the processes and products it creates and embeds adaptivity into both. DAASE will also create an array of new processes, methods, techniques and tools for a new kind of software engineering, radically transforming the theory and practice of software engineering. Contact: jrw@cs.stir.ac.uk

71


Equipment &  WIFI

WIFI Free WIFI access with individual login information will be available for all participants. In addition, eduroam accounts will also work at the conference site. Please note that there is no general PC room available this year.

EQUIPMENT FOR PRESENTATIONS There will be a notebook and a data projector in each of the conference rooms for your presentation. If you wish to use your notebook, it can be connected via VGA port. If you are a Mac user, remember to bring your adapter.

VOLTAGE AND ADAPTERS Voltage in Austria is 230v operating at 50 Hz frequency (North America is 110-120v, and in most European countries it is 220-240v) Wall sockets accept the two round-pin European style plugs (Plug C EURO or Plug F Schuko). Power cables from North and South America, the UK, and many parts of Asia will need plug adapters. Most laptops (and camera/phone chargers) have a voltage adapter which will adapt to the voltage automatically, so you probably only need a plug adapter if you don’t normally use a European-style plug. But please check your equipment as sometimes equipment bought in from outside Europe also needs a voltage converter as well as a plug adapter. You want to confirm this to avoid equipment damage!

72


Springerlink

Springerlink is available to all registered EvoStar 2013 participants during the period from 27 March - 24 April. You will first need to go to http://link.springer.com and register and create an account. If you are already a registered user, please log in with your user name and password and activate your token. The access code you will need is iIgaeCfXpiMO8dXG01jCgKF3BH0 The links to the five EvoStar volumes are below:

EuroGP  LNCS  7831          h#p://link.springer.com/book/10.1007/978-­‐3-­‐642-­‐37207-­‐0/page/1 EvoCOP  LNCS  7832   h#p://link.springer.com/book/10.1007/978-­‐3-­‐642-­‐37198-­‐1/page/1   EvoBIO  LNCS  7833 h#p://link.springer.com/book/10.1007/978-­‐3-­‐642-­‐37189-­‐9/page/1     EvoMUSART  LNCS  7834                         7834            h#p://link.springer.com/book/10.1007/978-­‐3-­‐642-­‐36955-­‐1/page/1   EvoAPPLICATIONS  LNCS  7835 h#p://link.springer.com/book/10.1007/978-­‐3-­‐642-­‐37192-­‐9/page/1  

73


RecepPon &  Conference  Dinner The EvoStar reception will take place on Wednesday evening, April 3, starting at 1930 at the City Hall Vienna in the “Wappensaal” on the first floor. This famous building was built in gothical style in the second half of the 19th century. It acts as the head office of Vienna's municipal administration and has magnificent halls and chambers. The City Hall is located very near the city center and can be reached easily by public transportation (tram lines D, 1, 71 and underground line 2). Refreshments will be served at the reception and as there is no formal d i n n e r p l a n n e d f o r We d n e s d a y evening, you are free to make your own arrangements. Meeting friends and colleagues is a social tradition at EvoStar and please join in, particularly if this is your first conference. You will make many new friends!

The Conference Dinner takes place on Thursday evening, April 4.

At 1815 coaches will depart from the conference venue for the 30 minute drive to Stift Klosterneuburg, a beautiful old monastery in the wine growing hills above Vienna looking down onto the Danube. A short guided tour of the historic monastery will take place and afterwards the conference dinner will take place at the nearby restaurant "Gastmeisterei" . You must remember to bring your conference dinner ticket when you board the coach. (in the envelope inside your conference bag). If you are unable to come to the dinner, please return your ticket to the conference desk so that someone else can benefit (we recycle spare tickets to accompanying persons). Coaches will return to the conference venue about 2230-2300, stopping off at a few city centre places en route.

74


EvoStar  opPonal  social  excursion

On Friday afternoon, April 5, an optional city walking excursion called "Ghosts, Hauntings and Vampires - Spooky Vienna". has been organised, leaving from the conference venue after lunch for the 10-15 minute walk to the city centre where the tour begins. This ghostwalk takes in some interesting sights in central Vienna. The duration is 2 hours (1430 - 1630) and tickets cost 10 Euros per person, sold from the conference desk on a first-come-first served basis (cash only). Programme description: "In two thousand years of history, many things have occurred in Vienna that cannot be explained by traditional science. Strange silhouettes can sometimes be seen in some places, spooky voices from underground can be heard in others. Objects fly through the air, diseases are mysteriously cured and people are bitten by nightly hunters emerging from their graves. In three chapters we talk about "common" ghosts and ecclesiastical supernatural phenomena, "imperial" ghosts and Viennese vampires and try to approach the truth behind legends in an amusing but nevertheless scientifically documented manner. You will learn on this Vienna ghostwalk why the first vampire was a Habsburg, what makes the difference between a black and a white ghost, which are the reasons for spooky voices on cemeteries and what you should never do when meeting a real ghost."

75


EvoStar parPcipants First Name Last Name Zvika Abramsky Alexandros Agapitos M Majid al-Rifaie Robert Alexander Alessia Amelio Mathis Antony Francesco Archetti Shahriar Asta Wolfgang Banzhaf Andreas Beham Una Benlic Víctor Berrocal-Plaza Leonardo Bezerra Benjamin Biesinger Franck Bigalet Christian Blum Markus Borschbach María Botón-Fernández Anthony Brabazon Mihaela Breaban Paolo Burelli William Bush Aleksander Byrski Stefano Cagnoni Michel Camilleri Alberto Cano Mauro Castelli Emir Causevic Jan Cerny David Chalupa Vic Ciesielski Jeff Clune Philippe Codognet Michael Cook Ernesto Costa Carlos Cotta Marcin Czajkowski Christian Darabos Ivanoe De Falco Antonio Della Cioppa Eelco den Heijer Owen Derby Napoleao Nepomuceno Doris Dicklberger Konrad Diwold Marcus Viniciusdos Santos John Drake Marc Ebner Dominik Egarter Gusz Eiben J Eisenmann Aniko Ekart Wilfried Elmenreich

Organisation City Ben Gurion University Beer Sheva University Collge Dublin Dublin Vividus Solutions LTD. London University of York York ICAR CNR Rende HKUST Clear Water Bay Università degli Studi di Milano-Bicocca Milano University of Nottingham Nottingham Memorial University of Newfoundland St. John's- NL FH OÖ Forschungs & Entwicklungs Gmb Wels LERIA Angers University of Extremadura Cáceres IRIDIA- CoDE- ULB Brussels Vienna University of Technology Vienna Sight'Up Torchefelon IKERBASQUE & Univ Basque Country San Sebastian University Bergisch Gladbach Ceta-Ciemat Trujillo University College Dublin Dublin Alexandru Ioan Cuza University Iasi Aalborg University Copenhagen Copenhagen Vanderbilt University Nashville AGH University of Science & Technology Krakow University of Parma Parma University of Malta Msida University of Cordoba Cordoba ISEGI- Universidade Nova de Lisboa Lisboa Vienna University of Technology Vienna Czech Technical University in Prague Prague Slovak University of Technology Bratislava RMIT University Melbourne University of Wyoming Laramie- Wyoming JFLI - CNRS/UPMC/University of Tokyo Tokyo Imperial College London London Universidade de Coimbra Coimbra Universidad de Málaga Málaga Bialystok University Bialystok Dartmouth College Lebanon ICAR - CNR Naples University of Salerno Fisciano Vrije Universiteit Amsterdam MIT Cambridge University of Fortaleza Fortaleza Vienna University of Technology Vienna Fraunhofer IWES Kassel Ryerson University Toronto University of Nottingham Nottingham Ernst Moritz Arndt Universität Greifswald Greifswald Alpen-Adria University of Klagenfurt Klagenfurt Vrije Universiteit Amsterdam Amsterdam The Ohio State University (ACCAD) Columbus Aston University Birmingham Alpen-Adria-Universität Klagenfurt Klagenfurt

76

Country Israel Ireland UK UK Italy HK Italy UK Canada Austria France Spain Belgium Austria France Spain Germany Spain Ireland Romania Densmark USA Poland Italy Malta Spain Portugal Austria Czech Repub Slovakia Australia USA Japan UK Portugal Spain Poland USA Italy ITALY Nederland USA Brazil Austria Germany Canada UK Germany Austria Netherlands USA UK Austria


EvoStar parPcipants Anna I A. Sima David Istvan Alexandra Jonathan Oliver Cyril James Francisco Martina Haobo Wenlong Patrick Ewa Roberto Mario Folino Fabian Kyrre Brian Ivo David L. Delaney Casey Igor Shihui Evert Fariba Tomohiro Malcolm Ignacio Damien Gwang-Soo Ting Bin Stephan Volker Johannes Vijay Patrick Yeonkyu Colin Ramprasad Merelo Maximos Salama Ahmed Christian Guido Krzysztof Gabriel Gerald Frédéric

Esparcia-Alcazar Etaner - Uyar Fagan Fehervari Fish Fisher Flasch Fonlupt Foster Fernández de Vega Friese Fu Fu Gabrielsson Gajda-Zagórska Gallea Giacobini Gianluigi Gieseke Glette Goldman Gonçalves González-Álvarez Granizo-Mackenzie Greene Grujicic Guo Haasdijk Haddadi Harada Heywood Hidalgo Hogan Hong Hu Hu Hutterer Imhof Inführ Ingalalli Janssen Jeong Johnson Joshi Juan Julián Kaliakatsos Khalid Kheiri Kloimüllner Kramann Krawiec Kronberger Krottendorfer Krüger

S2 Grupo Valencia Istanbul Technical University Istanbul UCD Belfield Alpen-Adria Universität Klagenfurt Klagenfurt Vanderbilt University Nashville Dartmouth College Lebanon Cologne University of Applied Sciences Dortmund Universite du Littoral Calais University of Idaho Moscow- ID University of Extremadura Mérida Colone University of Applied Sciences Gummersbach University of Birmingham Birmingham Victoria University of Wellington Wellington University of Borås Borås AGH University of Science & Technology Krakow Università di Palermo Palermo University of Torino Grugliasco ICAR-CNR Rende University of Oldenburg Oldenburg University of Oslo Oslo Michigan State University East Lansing University of Coimbra Coimbra University of Extremadura Cáceres Dartmouth College Lebanon- NH Geisel School of Medicine at Dartmouth Hanover Vienna University of Technology Vienna NCCA Poole VU Amsterdam Amserdam Dalhousie University Halifax University of Electro-Communications Chofu- Tokyo Dalhousie University Halifax Universidad Complutense de Madrid Madrid University of Limerick Limerick sunmoon univ asan-si Dartmouth College Lebanon- NH Vienna University of Technology Vienna Uni of Applied Sciences Upper Austria Wels Robert Bosch GmbH Stuttgart Vienna University of Technology Vienna INESC-ID Lisbon National University of Singapore Singapore sunmoon univ. asan-si University of Kent Canterbury BITS- Pilani Zuarinagar Goa University of Granada Granada University of Patras Athens University of Kent Canterbury University of Nottingham Nottingham Vienna University of Technology Vienna Fachhochschule Brandenburg Potsdam Poznan University of Technology Poznan FH OÖ Forschungs & Entwicklungs Gmb Wels Vienna Univ of Economics & Business Vienna Université de Strasbourg - LSIIT Illkirch

77

Spain Turkey Ireland Austria USA USA Germany France USA Spain Germany UK New Zealand Sweden Poland Italia Italy Italy Germany Norway USA Portugal Spain USA USA Austria UK Netherlands Canada Japan Canada Spain Ireland Korea USA Austria Austria Germany Austria Portugal Singapore Korea UK India Spain Greece England UK Austria Germany Poland Austria Austria France


EvoStar parPcipants EvoStar  parPcipants WB Markus Antonios Chuang Jean Zhipeng Jose Maria Broos Timmy Elena Yannis Michalis Michael Jon James Krzysztof Martin Julian Stefania Jason Alberto Amiram Malek Andreas Yuichi Malik Enrique Mateusz Miguel Michael Una-May Carlotta Qinxin Petrina Andrew Machado Dámaso Tobias Erik Frank Günther Marian Kate Denis Tobias Susanne Alvaro Mario Angélica Sergio Christian Julien Thomas Marc Kisung Nandita

Langdon Leitner Liapis Liu Louchet Lu Luna Maenhout Manning Marchiori Marinakis Mavrovouniotis Mayo McCormack McDermott Michalak Middendorf Miller Monica Moore Moraglio Moshaiov Mouhoub Müller Nagata Nairat Naredo Nawrocki Nicolau O'Neill OReilly Orsenigo Pan Papazek Parkes Penousal Pérez-Moneo Pilic Pitzer Plastria Raidl Rainer-Harbach Reed Robilliard Rodemann Rosenthal Rubio-Largo Ruthmair Sandoval-Perez Santander-Jiménez Schauer Schleich Schmickl Schoenauer Seo Sharma

University College London Vienna University of Technology IT University of Copenhagen Dalian University of Technology Gent University Huazhong Univ Science and Technology University of Cordoba Ghent University Cork Institute of Technology Radboud University Technical University of Crete De Montfort University University of Waikato Monash University University College Dublin Wroclaw University of Economics University of Leipzig University of York University of Parma iQBS University of Birmingham Tel-Aviv University Univ. of Regina Vienna University of Technology Tokyo Institute of Technology University of Gothenburg Instituto Tecnológico de Tijuana Poznan University of Technology UCD UCD MIT Politecnico di Milano Dartmouth College Vienna University of Technology University of Nottingham University of Coimbra UPM HTWK Leipzig University Univ of Applied Sciences Upper Austria MOSI - Vrije Universiteit Brussel Vienna University of Technology Vienna University of Technology Imperial College London LISIC- Univ Lille Nord de France Honda Research Institute Europe University of Applied Sciences University of Extremadura Vienna University of Technology University of Erlangen-Nuremberg University of Extremadura Vienna University of Technology University of Luxembourg University of Graz INRIA Saclay Ile de France Seokyeong University Austraian Natiional University

78

London Vienna Copenhagen Dalian Gent Wuhan Cordoba Gent Cork Nijmegen Chania Leicester Hamilton Caulfield East Dublin Wroclaw Leipzig York Parma Lebanon Birmingham Tel-Aviv Regina Vienna Yokohama Gothenburg Tijuana Poznan Dublin Dublin Cambridge Milano Lebanon Vienna Nottingham Coimbra Ponferrada Leipzig Hagenberg Brussels Vienna Vienna London Calais Offenbach Bergisch Gladbach Cáceres Vienna Erlangen Caceres Vienna Luxembourg Graz Orsay Seoul Canberra

UK Austria Denmark China Belgium China Spain Belgium Ireland Netherlands Greece UK New Zealand Australia Ireland Poland Germany UK Italy USA UK Israel Canada Austria Japan Sweden Mexico Poland Ireland Ireland USA Italia USA Austria UK Portugal Spain Germany Austria Belgium Austria Austria UK France Germany Germany Spain Austria Germany Spain Austria Luxembourg Austria France Korea Australia


EvoStar parPcipants Sara Kevin Anabela Robert Ana Andy Giovanni Andreas Mihai Arvis Jie Andrea G. B. Ronald Julian Alberto Paolo Jazz Alyxzander Leonardo Mario Vincent

Silva Sim Simões Sivley Soares Song Squillero Steyven Suciu Sulovari Tan Tettamanzi Thenius Togelius Tonda

INESC-ID Edinburgh Napier University University of Coimbra Vanderbilt University INESC Coimbra RMIT University Politecnico di Torino Edinburgh Napier University Technical University of Cluj-Napoca iQBS Dartmouth College Université de Nice Sophia Antipolis Artificial-Life Labratory- Univ of Graz IT University of Copenhagen INRA

Lisboa Edinburgh Coimbra Nashville- TN Coimbra Melbourne Torino Edinburgh Cluj-Napoca Lebanon Hanover Sophia Antipolis Graz Copenhagen Thiverval-Grignon

Portugal UK Portugal USA Portugal Australia Italy UK Romania USA America France Austria Denmark France

Turner-Baggs Vanneschi Ventresca Vidal

Halifax Lisbon Toronto Toulouse

Canada Portugal Canada France

Stefan Richard Simon Peter Jennifer Ransom John Bing Yu Bin Marcin Mengjie Nur

Wagner Watson Wessing Whigham Willies Winder Woodward Xue Yang Zagórski Zhang Zincir-Heywood

Dalhousie University ISEGI- Universidade Nova de Lisboa University of Toronto ONERA University of Applied Sciences Upper Austria University of Southampton Technische Universität Dortmund University of Otago Edinburgh Napier University MITRE University of Stirling Victoria University of Wellington Nanjing University Jagiellonian University Victoria University of Wellington Dalhousie University- FCS

Hagenberg Southampton Dortmund Dunedin Edinburgh Hanover Stirling Wellington Nanjing Krakow Wellington Halifax

Austria UK Germany New Zealand UK USA UK New Zealand China Poland New Zealand Canada

79


Notes

80


Notes

81


Notes

82


Notes

83


Notes

84

EvoStar 2013 Conference Handbook  

EvoStar 2013 conference information, programme and abstracts from accepted papers

Read more
Read more
Similar to
Popular now
Just for you