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Emergence DESIGN SEMINAR & WORKSHOP DOCUMENTATION

Director : Michael Weinstock Emeritus Professor : Dr. George Jeronimidis Studio Master : Evan Greenberg Studio Tutors : Elif Erdine Manja Van De Worp Mohammed Makki Design Team 5 : Mithuna Murugesh Varvara Vasilatou

EMERGENT TECHNOLOGY & DESIGN ARCHITECTURAL ASSOCIATION

Abstract The research focuses on the exploration of evolutionary design techniques in generative algorithms through advance compu-tation. The concept of â€˜Evo-Devoâ€™ and the biological process of growth and evolution in living organisms formed the pri-mary basis of this investigation and have further been translated into active simulation in evolutionary computation. The use of evolutionary solvers and genetic algorithms in design has introduced many advantages in comparison with traditional design methodologies. Therefore, the study aims at documenting the process, analysis, strategies and results through the application of the natural principle of growth and development into emergent design techniques and processes. The experiments were divided into three sequences. Initially, a cube was selected as a primitive to be modified by the genes, which in this case consisted a series of geometrical operations. The resulted individuals were evaluated according to the fitness criteria that represented the aimed objective and selected for the next step. The genomes of the selected phenotypes subsequently were combined to produce new individuals. In sequence two, the urban plot of Shibam, Yemen was selected as primitive and new conflicting fitness criteria, genes and breeding strategy were introduced, accordingly. This sequence relied on the simulation through Octopus, a Grasshopper plugin used for applying evolutionary principles to parametric design and problem solving. The third sequence of experiments was based on the knowledge acquired from the previous sequences and the selected strategy corresponding to the real environment and urban parameters. A superblock of 4 in the city of Shibam was used for further investigation. The common ground throughout the sequences is that this design approach does not aim to reach a single optimal solution that could operate in any environment but produce a range of solutions which are optimised in relation to a particular design environment and specific evolutionary goals. Keywords: evolution, computation, growth, development

Content 1. I N T R O D U C T I O N 2. B A C K G R O U N D

p.01 p.02

Sequence I

3. S T R A T E G Y G E N E R A T I O NS I-V O B S E R V A T I O N S

p.06 p.09 p.14

4. R E V I S E D S T R A T E G Y G E N E R A T I O NS VI-X

p.15 p.16

O B S E V A T I O N S & C O N C L U S I O N S

p.22

S e q u e n c e II

7. S H I B A M Y E M E N STRATEGY MODEL SET-UP

p.26 p.28 p.32

8. G E N E R A T I O N III

p.33

9. G E N E R A T I O N VI

p.37

10. G E N E R A T I O N X

p.41

11. O B S E R V A T I O N S & C O N C L U S I O N S

p.46

S E Q U E N C E II

12. S T R A T E G Y MODEL SET-UP

p.50 p.54

13. G E N E R A T I O N 30

p.55

14. G E N E R A T I O N 60

p.59

15. G E N E R A T I O N 100

p.63

16. O B S E R V A T I O N S & C O N C L U S I O N S

p.67

17. C O N C L U S I O N S

p.70

Introduction

According to Evo-Devo ‘the key to understanding form is development, the process through which a single called egg gives rise to a complex multi billion called animal’1 The entire research is integrated into three sequences with increasing complexity in terms of their body plan, evolutionary growth and breeding strategy, entwined with relevant logic, computational tools and techniques. The experiments are initiated by a primitive, that is being evolved through a series of basic “modifiers”, the genes, to produce a population of individuals. The combination or arrangement of these genes forms the genome that deliberately highlight a particular feature in each individual generation. Once the generation is formulated, the individuals are evaluated according to specific fitness criteria, set before the execution of the simulation and unique to each sequence. Each subsequent generation progresses based on its predecessor through selective ranking. This ranking assisted in determining the possible breeding strategy, for the future generations to achieve optimum fitness interns of environmental factors and variation in the individuals. The concept of mutation plays an important role in the evolution of populations from one generation to another. The alteration in the genomes leads to increased randomisation of varied individuals and can produce unpredictable results.

1. Source: Carroll, Sean B. (2005): Endless forms most beautiful. The new science of Evo Devo. NY: W. W. Norton & Company, Inc.

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Design Team 8 _ Mithuna Murugesh, Varvara Vasilatou Emergence 2016

Key Words

To understand and follow the entire process, a few keywords are cited: • • • • • • • • • •

Phenotype: the geometry of the forms that the simulation will produce Gene: a single parameter that controls the type and intensity of modification to the phenotype Generations: the number of iterations in any single run of simulation Population: the overall number of individuals per generation Fitness Criteria: the criteria on the basis of which phenotypes are evaluated Crossover: the exchange of genes from different phenotype when breeding to make a descendant Elitism: the number of dominant solutions selected to generate the next population Mutation: the procedure of allocating random modifiers to the genes Mutation Probability: the probability of a gene to be mutated Mutation Rate: the percentage of the selected individual’s genome that will be mutated

2. Evolutionary Computation

Evolutionary computation is an advance reach of computer science that explores a series of complex solutions derived from discoveries made in natural biological development of species. It offers a plethora of research preferences and are mainly based on the biological models generated by renowned scientists like Darwin in ‘The Original Species’2 and Carroll in ‘Endless Forms Most Beautiful’.3 In this research, the sequence progression and simulation greatly relied on harnessing heavy computation, as the individuals and generations to be evaluated were significantly high in number with multiple fitness criterion. The research initially commenced with standard operations in ‘Rhinoceros’ and further advances to ‘Grasshopper’ and ultimately ‘Octopus’ to simulate, analyse and achieve multi objective optimisation.

Evolutionary Development ‘Evo-Devo’

The paradigmatic shift from the Darwinian theory of evolution in 1859, to D’Arcy W. Thompson view ‘On growth and form’4 in 1917, to Sean B. Carroll in 2005 led to the rise of ‘Evo-Devo’ as an integrated branch of science which witnessed the concatenation of the theories behind the evolution and embryological development.

Background

Aims and Objectives

The primary aim of this research is to demonstrate and evolve a series of generations that progress in accordance to multiple fitness criteria. The strategies and understanding are developed according to the ‘Evo-Devo’ theory and form the basis of laying the foundations of evolutionary computational decisions. The secondary aim encompasses the attempt to establish a relationship between the theories of Evolution and embryological development into the architectural realm to generate emergent design economies and develop growth strategies for the same. In order to achieve this aim extensive use of evolutionary computational tools and techniques is made. Analysis based on somoatusons and tests to predict the optimal output are implemented to generate accurate results.

Scope

The scope of this research is to progress from a singular parent primitive to a ‘block’ and then to a ‘super block’ with increasing and contradicting criteria of fitness. The. Entire research demands explicit use of heavy computational power, thus it is limitated to a maximum of three fitness criteria and involved rationalising the primitive in each sequence progression.

This field of biology construes and compares the growth and development of different organisms to ordain a totemic inter-relationship between them.

Body Plan

The term ‘body plan’ is used to define the unique arrangement of body parts, in any organism and its growth pattern along a specific axis. It is a diagrammatic experience of an organism’s body constituents that enables researchers to identify and distinguish the morphological differences occurred in that organism over a period of time and thus, compare these parts in the configuration of classes. This process gets more complex in terms of evolution as the genome’s sequences are modified and multiplied, acclimatising to the present environmental changes and validating its existence.

2. Darwin, C (1859): On the Origin of Species by Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life. London: John Murray, Albermarble Street. 3. Carroll, Sean B. (2005): Endless forms most beautiful. The new science of Evo Devo. NY: W. W. Norton & Company, Inc. 4. Thompson, D’Arcy W. (1917): On Growth and Form. Cambridge: University Press. Emergent Technologies & Design | 2 AA School of Architecture

Sequence

A. Primitive: CUBE D. Gene Regulation Strategy

B.B Body Plan

pattern genome

A . PRIM ITIVE: C UB E

Scale uniformly by 1.3

1,2

mly 3

Rotate on Z axis by 30

B. BO DY PLAN & Base point: centre of gravity G EN E REGULAT ION ST RAT EGY

C. Gene Pool

C. G E N E POOL

30o

Linear array 3 units

Base point: centre of gravity Destination point: upper right corner

Scale non-uniformly on X axis by 1.5

teria

Scale non-uniformly on Z by 1.3 ize surface area exposure to aaxis specific vector

Scale uniformly by 1.3 FC.II:

Rotate on Z axis by 30

Base point: centre of gravity minimize overall volume

plan view

Polar array 4 units by 30o Base point: left corner

5Â |

Linear array 3 units

Base point: centre of gravity Destination point: upper right corner

Design Team 8 _ Mithuna Murugesh, Varvara Vasilatou Emergence 2016

Scale non-uniformly on X axis by 1.5

y 2

yarray 4 units 4 units by 30 byo 30o Linear Linear arrayarray 3 units 3 units

se int:point: left corner left corner Base Base point:point: centrecentre of gravity of gravity Destination Destination point:point: upperupper right right corner corner

ScaleScale non-uniformly non-uniformly on Xonaxis X axis by 1.5 by 1.5

3. Sequence I| Strategy

Introduction

In sequence one simple Rhino experiments are carried out, in order to gain a understanding of evolutionary computation and the concepts of Evo- Devo.

E . FI TN ESS CRITERIA

Fitness ness Criteria Criteria

FITNESS MAX CRITERION SURFACE AREA FC.I: : maximize maximize surface surface areaarea exposure exposure to a to specific a specific vector vector EXPOSURE 01

Procedure Primitive

FC.II: FC.II:minimize minimize overall overall volume volume

The cube is selected as the primitive geometry for the creation of the individuals in each generations and all experiments are conducted in this primitive geometry and evolved through a series of basic modifiers taken from Rhinoceros syntax to produce phenotypes.

Genome

plan view plan view

The set of genes arranged or combined together forms the genomes that deliberately actuate a series of transformations to make individuals different from the other and in some cases also the same. In this case the genome consists of 5 genes.

Gene Pool

FITNESS

MIN FC.II: minimize overall volume CRITERION OVERALL

VOLUME

In order for the individuals to be equally comparable, a set of modification rules are configured to act upon the primitive for the formation of the individuals.

Gene Regulation Strategy

The positioning of each gene in the genomeâ€™s sequence defines the body part on which this gene acts upon.

Fitness Criteria

The individual are evaluated based on specific conflicting fitness criteria. The first fitness criterion is maximising the surface area exposure to a specific vector. The second criterion is to minimise the overall volume. Observations from each generation informs the decisions taken to pursue to the next generation.The overall results are analyzed and compared on the 5th and 10th generation.

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F. KIL L IN G ST RAT EGY

E. CROS S OV ER L OG IC

F. Killing F. Killing Strategy Strategy F. F. Killing Killing Strategy Strategy

E. E. Crossover Crossover Logic Logic E. E. Crossover Crossover Logic Logic Crossover Crossover Point Point

Crossover Point Crossover Point E. Crossover Logic

F. Killing Strategy FC.IFC.I FC.I FC.I

80% 80% 80% 80%

XX XX XX XX XX

FC.I

FC.II FC.II FC.II FC.II 80%

Fittest Fittest Fittest Fittest

Least Least Fit Fit Least LeastFit Fit

Fittest

H. BRE E D ING ST RAT EGY 2-5) (Generations: 2-5) Strategy H.(Generations: Breeding (Generations 2-5)

1 Fittest Fittest 11 Fittest Fittest Fittest 2 21 222 3 3 333 4 4 444 5 5 555 6 6 666 7 7 777 8 88 88 9 99 99 Least Fit Fit 10 10 Least 10Least Least 10 LeastFit Fit 10 1

Y Y

X Y

(Generations: (Generations:2-5) 2-5)

Type: Duplication Probability: 10% Type: Type: Duplication Duplication Type: Duplication Type: Duplication Rate: 20% (last gene) Type: Duplication Probability: 10%10% Probability:

Probability: 10% Probability: 10% 10% Probability: (last Rate: 20%20% (last gene) Rate: gene) Rate: 20% (last gene) Rate: 20% (last gene) Rate: 20% (last gene)

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Design Team 8 _ Mithuna Murugesh, Varvara Vasilatou Emergence 2016

21 2 22 32 3 33 43 4 44 4 Generation Generation I I5 555 Generation I6I5 6 Ranking Ranking Generation Generation Ranking Ranking I 6 Ranking 6 6 7 7 7 77 8 8 8 88 9 9 9 99 10 10 10 10 10

21 32 43 54 65 6

11 1 11 22 2 22 33 3 33 44 4 44 5 Generation Generation Generation IIII II5 555 Ranking Generation Ranking Ranking II Generation II6 6 6 Ranking Ranking 66 77 7 7 8 7 8 8 9 88 9 9 10 99 10 10 10 10

13 13 14 14 15 15 16 16 17 17 18 18 19

2 Fittest 12 22 3 23 33 34 4 44 45 5 55 56 6 66 6 7 7 77 7 8 8 88 8 9 9 99 9 10 10 10 10 10 11 11 11 11 11 12 12 12 12 12 13 1313 13 13 14 1414 14 14 15 1515 15 15 1616 16 16 16 1717 17 17 17 1818 18 18 18 19 19 19 19 19Least Fit 20 20 Least Least Fit Fit20 20 Least 20 LeastFit Fit

2 22 3 33 4 44 5 55 6 66 7 77 8 88 9 99 10 10 10 11 11 11 12 12 12 13 13 13 14 14 14 15 15 15 16 16 16 17 17 17 18 18 18 19 19 19 20 20 20

I. Mutation Strategy

I. Mutation I. Mutation Strategy Strategy 2-5) ST RAT EGY I. Mutation M U(Generations: TATION I. Strategy I.(Generations: Mutation Strategy (Generations: 2-5) 2-5) (Generations 2-5)

Formulation of FC.I-FC.II Formulation of FC.I-FC.II Formulation of of FC.I-FC.II FC.I-FC.II rearrangement of current & former generations’ population rearrangement ofFormulation current & former generations’ population Formulation FC.I-FC.II rearrangement of & former rearrangement of current currentofof &FC.I-FC.II formergenerations’ generations’population population Formulation FC.I FC.I rearrangement of current &former formergenerations’ generations’ population rearrangement of current & population FC.II FC.II FC.I FC.I FC.II FC.II 1 1 1 1 FC.I Fittest 1 1FC.II 1 1 Fittest 11 11 11 Fittest 11 Fittest

(Generations: 2-5)

FC.IFC.IFC.II FC.II FC.I FC.II FC.I FC.I FC.II FC.II

X 40XX% |XY60%YY YY Y XX XX YY YY YY

Least Fit

H.H. Breeding Breeding Strategy Strategy H. Breeding Strategy H. Breeding Strategy (Generations: (Generations: 2-5) 2-5)

1 11 2 2 1 22 2 3 3 33 3 4 4 44 4 5 5 Generation Generation 55 5 Generation Generation Ranking Ranking Generation 6 6 Ranking Ranking Ranking 66 6 7 7 77 7 8 8 8 88 9 9 9 99 10 1010 10 10

X X

Y YY YY YY YY Y YY YY YY YY YY

FC.II

40 % 40|%60% | 60% 40 40% %||60% 60%

Crossover X XX XXPoint XX XX X

X X

X XX XX XX XX X XX XX XX XX XX Y

Y Y

Y YY YY YY YY Y YY YY YY YY YY

X Y

X XX XY YY YY YY Y XX XX YY YY YY YY

1 2

2 22 3 33 3 4 4 44 4 5 5 Generation I I 55 Generation Generation II Ranking Ranking Generation 5 6 6 Generation I Ranking 66Ranking Ranking 6 7 7 77 7 8 8 88 8 9 9 99 9 10 10 10 10 10 2 3

1 11 2 2 2 22 3 3 3 33 4 4 4 44 5 5 5 Generation II II II 5Generation 5 Generation Generation Ranking Ranking II Generation II 6 6 6 Ranking Ranking Ranking 66 7 7 7 77 8 8 8 88 9 9 9 99 10 10 10 10 10 1

2. Sequence I| Strategy The key concepts of breeding and killing strategies, crossover and mutation rates and ﬁtness criteria that arise from evolutionary developmental biology (Evo-Devo), in relation to the population, intensive and topological, were applied to virtual three-dimensional objects and multiple variations of results were produced, which were every time evaluated according to a different evolutionary goal.

Killing Strategy

The killing strategy (80%) is used to kill 9 of the least fit individuals within each fitness criteria ranking, or else selecting the fittest individual for each established fitness criteria.

Cross-over Logic

According to our one-point-crossover logic, the first 40% of the first parent’s genome (2 genes) is combined with the last 60% of the second parent’s genome.

Breeding Strategy

The fittest two individuals are selected for breeding to create the subsequent generations. The fittest individual from fitness criteria 1 breeds with the 5 fittest individuals from fitness criteria 2 and vice versa. For the generations 2-5, the individuals of the current and the former generation are ranked together to select the fittest individuals for breeding.

Mutation Strategy

The selected mutation type is duplication, applied always on the last gene (20% mutation rate) and affecting only one indi-vidual of the population (10% probability).

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Sequence I| Generation I

G.1.2

G.1.1

1 364.2 m2 9 1814 m3

7 137.7 m2 6 1186.5 m3

G.1.7

G.1.6 8 135 m2 3 830.2 m3

10 72.3 m2 2 819.3 m3

G.1.3 5 204.6 m2 7 1205.4 m3

2 363 m2 10 1927.6 m3

G.1.9

G.1.10

3 258.6 m2 8 1393.5 m3

9 104.9 m2 1 608.8 m3

4 244.4 m2 5 1165.6 m3

FC.II min Overall Volume

General

The first generation is created by a random combination of six genes from the gene pool to record the results of different combinations as seen in the phenotypes. Generation I produced 10 individuals, which are ranked according to each fitness criteria separately. The main aim is to achieve maximum surface exposure along a vector while maintaining minimum overall volume. The two fittest individuals are carried forward to the next generation.

Observations

Generation 1 creates random phenotypes with no pattern that can be clarified. However, the ranking of the individuals suggest that some gens are adequate for specific fitness criteria, as expected. For example, the linear array works in favor of maximizing the surface area exposure. In the morphological classification, the gene of linear array (D) and polar array (E) are most dominant. Design Team 8 _ Mithuna Murugesh, Varvara Vasilatou Emergence 2016

6 165.4 m2 4 1156.8 m3

G.1.5

G.1.8

FC.I max Surface Area along a vector

9Â |

G.1.4

Sequence I| Generation II

G.2.1

G.2.2

8 105 m2 2 608.8 m3

1 364.2 m2 9 1814 m3

G.2.6

G.2.7

9 91.3 m2 3 742.8 m3

10 77.4 m2 5 1080.57 m3

G.2.3

G.2.4

3 215.2 m2 8 1784.7 m3

G.2.8 7 129 m2 1 563 m3

4 171.8 m2 10 1944 m3

G.2.5 6 148.5 m2 4 1032.7 m3

G.2.9

G.2.10

5 161 m2 7 1315.8 m3

2 244.4 m2 6 1165.6 m3

FC.I max Surface Area along a vector FC.II min Overall Volume

General

Generation II is created according to the predefined breeding logic and is ranked according to our fitness criteria.

Observations

The genome pattern indicates that the first two genes, ‘FF’ and ‘BA’, are the same, each for 5 individuals. This is due the breeding strategy selected, that requires only two individuals to be combined to the rest. Within the ranking for fitness criteria the best phenotype is also one of the worst for the other fitness criteria, suggesting the fitness criteria are conflicting. Morphological Observations Once again the linear and polar array genes are most dominant in the morphological classification. Emergent Technologies & Design | 10 AA School of Architecture

Sequence I| Generation III

G.3.2

G.3.1

1 364.2 m2 9 1814 m3

7 131.2 m2 6 612.9 m3

G.3.7

G.3.6 2 279.8 m2 9 2371.8 m3

9 126.95 m2 2 603.6 m3

G.3.3 3 279.8 m2 8 2371.8 m3

G.3.8 6 164.8 m2 5 940.4 m3

FC.I max Surface Area along a vector FC.II min Overall Volume

Observations

The first 40 percent of the genomes, â€˜BAâ€™, are identical for all the individuals, due to the breeding logic used. Once again, within the ranking for fitness criteria the best phenotype is also one of the worst for the other fitness criteria, suggesting the fitness criteria are conflicting. In this generation, two groups of phenotypes are starting to emerge, each one satisfying one fitness criterion, either maximising surface area along a vector or minimising volume. This observation, alongside the fact that the actual values of the fittest individuals are augmenting and decreasing respectively to its fitness criterion. suggest that not all the genes work for both criteria, so that the experiment is driven to extremes. The mutation introduced on the gene do not seem to affect the variation, though in this generation it is evident that the mutation mostly works in favour of minimising the overall volume. 11Â |

Design Team 8 _ Mithuna Murugesh, Varvara Vasilatou Emergence 2016

G.3.4 4 258.9 m2 10 2508.6 m3

G.3.9 10 77.37 m2 4 720.4 m3

G.3.5 5 244.4 m2 6 1165.7 m3

G.3.10 8 128.7 m2 1 563 m3

Sequence I| Generation IV

G.4.1 6 129 m2 2 563 m3

G.4.6 5 244.4 m2 6 1165.7 m3

G.4.2 1 364.2 m2 8 1814 m3

G.4.7 7 126.9 m2 4 603.6 m3

G.4.3 2 284.2 m2 9 2371.8 m3

G.4.4 3 284.2 m2 10 2371.8 m3

G.4.8 8 121 m2 3 599 m3

G.4.9 9 102 m2 1 375 m3

G.4.5 4 259 m2 7 1208 m3

G.4.10 10 77.36 m2 5 720.4 m3

FC.I max Surface Area along a vector FC.II min Overall Volume

Observations

The first two genes, being the same, are no more affected by the experiment. In the last two generations, some patterns in the gene regulation are becoming more evident. The F gene is almost driven to extinction, whereas other specific gene combinations are starting to become more evident. There are many identical individuals in the same generation and along this and the former one. In particular, G.4.3-G.4.4-G.3.6-G.3.3, G.4.5-G.4.6, G.3.10-G.4.1. This can be partly explained by the killng strategy that retains two individuals unchanged for the next generation. Patterns such as DE, EDD, ECE are presented in the fittest individuals for FC.1, whereas, the gene combination of AC, CC seems to be adequate for FC.2. Morphological Observations In this generation, two groups of phenotypes are being further exaggerated. The variations on the phenotypes present less flunctuations suggesting that the simulation might have be driven to convergence, by approaching a local peak. Emergent Technologies & Design Â | 12 AA School of Architecture

E D A F C B F

E A F C

B D

E D C

D A

D C D C

A F E

A C

F E D E

Generation 5

C F A C

E F DF B DD A E E A D

F F A E A A F E F D DD E A F

C E

D C F B F A D E BA B B D A E C A F F BF A FD BFE ACAB DDA ACC C D C DF A F B C A A E E A D D CF DF CBB FCA FAD BE AA E B D B B A AF E BF A AF D A EC BD DA

F F B C A E F D F A AC E A C

B D

B D E B A A A E FA F B C A D D C A C B A BA BAE AAAB FEA AAB B C A CF F F B F B A C D A E A BB BA DCB EF A AF A FBE AA D F E F B A DD CA A BA CA C CA D BC

B A C F F FF D F C F F BF C A B B

C BD A5 CB A A E C A F F F C D B C A C D BC A B A A E F A A B B A Generation A C A F A F E F A FB FA CDB DAA CCC BD AC A B E A B A EF B AA C BB A A C BA A A DB E A B

B A D A C B B D A E CA F F F

B D A C AB CA CD A C A A A E B A A B CGeneration B5A A E A B A D C D B C D A A C C D C BB AA AF B EAA ABB BC AA Generation F A D A A C E F BA BC 5A CB DA B

B C A C E C E F A A C C B A F A B Generation 5 B A C F B B A A F B C A D DC A C B A A B E A B A F A A B B C A Generation AB E A A C BF AF D B D ABC AAC CD CA B C A B A 5

B A A CB CA D A B B F A F E C A E C E B A C B A A E A B A AB CA CD BA AC A B C A C E A B A A E F A A B Generation 5 B A B A A E C A F F

F A C A

A A

B B

E E

G.5.6 A D

B A

B B

E C

D E

B A

A E

D D

E D

A D

D E

E A

B A

A D

D E

A A

B A

A D

D A

D C

B B

B A

C D AG.5.8 D D C

124.6Am2 C B8 A C m D3 C A5 610

6 129 m2

B 3 A563 D m3 D

A CSurface C A max FC.I Area along a vector

FC.II min Overall Volume

Observations

Within the ranking for fitness criteria, the best phenotype is again the worst for the other fitness criteria. The gene ‘F’ has completely disapeared from the genome. There is a marked discrepancy between the two groups of extremes. The identical phenotypes are again evident, G.5.5-G.5.6 and G.5.10-G.5.1. The mutation has an impact on this generation, by affecting the G.5.4 and driving into change of the fittest individual for surface exposure, that till this generation was remaining unaltered. 13 |

Design Team 8 _ Mithuna Murugesh, Varvara Vasilatou Emergence 2016

A A

3 284.2 m E D m3D A9 2371.8

C C

G.5.7

D D

A E

A A

B A

2 364.2 m 8 1814 m3

B B

D C

A A A A

B B

C C

A C D

AG.5.9 C C C D C

7 127 m2

D D

D E

A4 603.6 A Cm3 C

D C

B A E D F E D C B F A G.5.5 A A

B B

B D

A D

B B

D E

C C

E A

E C G.5.4

1 375.9 m A 3709.5 D Em3 A 10

A B

2 B 5 A 259Dm D C A7 1208 D Am3 C

A D

D D

A A

A E C E AG.5.3 C BC A

D E

E E

B A

B E C

B F

BG.5.2 Generation 5 A A C

9 102 m B2 375 A m E3 C

Generation 5 B A AG.5.1 E A

B F F F A EB D

B B D E A DAB AEA CAE BD AD E C D DF E BA A A A FB D FC BD CF

Sequence I| Generation V

B F

D E B D D

E D

A D

A A

D D

A E

C A

B B

A A

D D

D A

C C

B B

A A

C D

D D

C C

B B

A A

A C

C D

C C

4 259 m D mE3 B 6A 1208 2

G.5.10 A

10 102 m 1 375 m3 2

Sequence I| Observations Genetic Information Table G.1

G.2

G.3

G.4

G.5

1 2 3 4 5 6 7 8 9 10 Population Distribution G.1-G.5 FC.1 Standard Deviation Graph, G.1-G.5

0.006 0.005

0.001

0.004

0.0008

0.003

0.0006

0.002

0.0004

0.001

0.0002

0 -200

-100

G.1: μ = 1210.77, SD = 397 G.4: μ = 1179, SD = 717.9 G.2 SD Steps G.5 SD Steps

100

200

300

G.2: μ = 1205.2, SD = 478.9 G.5: μ = 1302.6, SD = 1302.6 G.3 SD Steps

400

FC.2 Standard Deviation Graph, G.1-G.5

0.0012

500

G.3: μ = 1367.2, SD = 772 G.1 SD Steps G.4 SD Steps

General Observations & Analysis

The standard deviation graphs of the population for each fitness criterion suggest that the variation throughout the experiment gradually increases. This is depicted especially in the graph of FC.2. Nevertheless, our observations throughout the procedure implied quite the opposite. Our experiment didn’t drive into variation, but into convergence of each fitness criteria separately and the creation of two groups of individuals in the extremes. Thus, this observation demonstrates that the great variation in the graph may not refer to an equal distribution of the population. The group of the extremes indicates as well that there is no actual interaction between the fitness criteria and thus, no optimal solution-individual satisfying both. This created doubts about the validation of the experiment’s initial set-up. Indeed, revising our strategy, we realised that our genes didn’t affect both the fitness criteria. There was a lack of genes to decrease the volume, which explains why the average values of the FC.2

600

0 -3000

-2000

-1000

G.1: μ = 1210.77, SD = 397 G.4: μ = 1179, SD = 717.9 G.2 SD Steps G.5 SD Steps

1000

2000

G.2: μ = 1205.2, SD = 478.9 G.5: μ = 1302.6, SD = 1302.6 G.3 SD Steps

3000

4000

5000

G.3: μ = 1367.2, SD = 772 G.1 SD Steps G.4 SD Steps

are gradually increasing instead of decreasing. From the standard deviation graph it can be seen that the average of FC.1 (area) increases slightly, without a significant differentation. Nevertheless, on calculation it is noticed that the ratio between FC.1 and FC.11 increases, suggesting that the experiment is actually trying to achieve the objective. In the genomes’ configurations’ pattern, we noticed that the undertaken breeding strategy in correlation to the cross-over logic (40%), led to the repetition of the 1st 40% of the genome throughout the whole population, from G.3 and so forth. There was a wide repetition of identical phenotypes in the same generation and throughout the overall population, due to the breeding and elitism strategy. This impact was compounded by the combination of two generations’ individual for the breeding of the next one. Lastly, gene F frequency gradually decreases to extinction in the G.5, suggesting that this gene might didn’t have a great impact on either of our goals. Emergent Technologies & Design | 14 AA School of Architecture

Revised Strategy

After the analysis and the observations manifested in the previous generations, our goal is orientated in producing more variation in terms of interaction between the individuals, that could help to tackle the issue of the extremes inside the population, and thus, be able to reach optimal solutions for both fitness criteria. Therefore, our intent is to drive the experiment into the emergence of a more equally distributed, in variation, population. For this purpose, the breeding strategy is altered and a new mutation strategy is put in action.

Revised Breeding Strategy

For G.6-G.10 the killing strategy (80%) is remained unchanged, with the fittest individual of each fitness criteria moving forward to the next generation. However, unlike in generation 1 to genration 5 the fittest individuals of each ranking are not used to cross breed instead a new pattern of the second fittest individual from FC.1 is crossed with the fifth most individual of FC.2 , the third fittest individual from FC.1 is crossed with the fourth most individual of FC.2 and vice versa. This allows for no repition in terms of the genome pattern as was experienced from G.1-G.5.

Revised Mutation Strategy

A drastic mutation strategy affecting 3/10 individuals of the generation is introduced to the system. in the type of addition. According to this, a new random gene is added at the beginning of the genome. With this motion we aim to solve our previously incurred issue of the repetition in the genomeâ€™s first part throughout the whole population.

New Gene

For tackling the lack of genes that work for minimising the volume of the individual, a new gene is introduced into the system, through the act of mutation, that follows the simple Rhino command of scaling uniformly by 0.7 in all axes

A. New New Breeding Breeding Strategy Strategy A. (Generations: 6-10) 6-10) (Generations:

A. New Breeding Strategy

A . N EW B R EED IN G S T R AT EG Y (Generations: 6-10) FC.I FC.II FC.II FC.I (Generations 6-10) Fittest 11 11 Fittest 2 FC.I 2FC.II 2

Generation Generation Ranking Ranking

Generation Ranking

10 10

Fittest

Least Fit Fit 8 Least

Least Fit

B. New New Mutation Mutation Strategy Strategy B. (Generations: 6-10) 6-10) (Generations:

B. N EW MUTAT ION S T R AT EG Y

B. New Mutation Strategy

(Generations Type: Insertion 6-10) Type: (Generations: Insertion 6-10) Probability: 30% Probability: 30% Type: Addition Rate: 20% Rate:Type: st 20% Insertion

always on on 11st gene gene genome 30% Probability: ofof genome always Probability: 30% Rate: 20% (first gene)

Rate:

20%

always on 1st gene of genome

X X X X X X X X X X X X X X X YY YY YY YY YY Crossover Point Y YPoint Y Y Crossover

X X X YY YY YY CC X C

X Y

Crossover Point

I N T ROD UCTofION OF gene A N EW G E N E C.C.Introduction Introduction new C. of aa new gene G

G C. Introduction of a new gene G

Scale uniformly uniformly by by 0.7 0.7 Scale 15Â |

Design Team 8 _ Mithuna Murugesh, Varvara Vasilatou Emergence 2016

Scale uniformly by 0.7

D A

F C ED DC AF

C F

C B DB CD FE

F A

B F BF DC ED

C A

A FE FD DD E DF

E FD FA AC E CF

D F

C F F E F D F A BA CAE EA D

B C E F A F D F B A D C DF CF BFB AFC D A B E

A C C D D AA C BB D C F F C AB EB DB FA D FE A EB C AA FA B D DA

B EB

A AA

B C

D E A D F C F F BB F AC DA E

F D A AB C A D C F A BF C B BB E AA A B A A A B BB DA BEC AAF A F B E F AF DF EB CD AC A CA AC B C AA DB D

B EB

A AA

D B

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A AB B FA FAB BCEA AD AF DC

Generation 9 96.26m2 B

3 465 m3 A

B D

D D

G BB AA

D A

E C

A D

E C

B B AA EA

D E

D A

B G AB DA

A E

C A

B BA AC

D E

C A

B GA BC

C D

D 2

A A

B B D B G B

G.6.6

B B AA AC

5 244.4 m

G.6.7

D A

C A

10 92 m

G 6B 1165.7 A m A3 C A

EB AA

Sequence VID A Generation E A B A C DB CA I| B A C

C C

A 2 C387.3 D mC3

B G

G C

B A

B BA

B B

A BA AE CA F C C

Generation 6

2 367 m A E D D 9 3709.6 m3

B D

Generation 6 B

G.6.2 Generation 6

G.6.1

A BFAC AB AE BB AA B A E A AAF BBF C

A BA AE FA A

Generation 6 B

A A E B A A FE FA F C D C B A F BB AA C A B A E A A B A B A A E F F C C C C B A B A D F C C F F F F D B BC AA C B B E A B A A C B A D D F C B A C D F F B A A E A B B DB EA AC F F F B A DB AA CC D C B A B CB AA C D C B A D A D BC C Generation 6 B A A E A B A A E A B A D A C CAB A AA A ECC B F FB FA B C A BDBB C D A C CC DB B CAAC CCA D C B B ABA AAC BEFBAAFAA B BD B D AFC AF D CD CAA FC F FB AA F BF BCB BDDA AE B A A C C Generation 6

Generation 6

A B

B A D E D D D AG.6.3 A C C

B B

A A

E E

D D D2 D

1 407.8 m

A 10 E 3736.6 D Dm3 G B A E D

F Generation 6B B A E C

B G

E D G.6.4

3 284.1 m

B B

A B

A A

E D

A E

B B

A A

C E

C A

A G

C B

D A

C A

C C

A B

C A

B B

8A 121 D mC 4A 600 C m3D

C C

A A

E E

A E

C A

A A

G.6.8 D C

D A

C C

D D

G.6.9 B A D A

C D

D D

A C

A C C

A C A

B A D D

A F A

E F E

BG.6.5 A E

G.6.10

6 127 m

4 259 m2 E3 A A m 7A 1208

C C

A C B

B B

A C

A G B C B G

G 5 B603.6 A m3D

F B

B A D D D

C A A D B D D

A D m3E A B8 2371.8 B A E D D

A D

E E

A D

D D

D A

A B

C D D 6B ABA C C B AC A C DF BAF AC A FCA FGeneration AD B A EA C AA B A A C C B A E D D D

7 123.2 m 1 375 m3

FC.I max Surface Area along a vector

FC.II min Overall Volume

General

Generation VI is created according to the revised breeding logic and new mutation strategy of addition of randomised genes to introduce variation. This genereation is ranked according to our fitness criteria.

Observations

In this generation due to revised breeding strategy there are no identical individuals within the generation and also in relation to previous generations. In terms of genes, the ‘BA’ pattern is present in the fittest phenotypes for both criteria. It is clear, within the ranking for fitness criteria the best phenotype is also one of the worst for the other fitness criteria, suggesting the fitness criteria are conflicting. It appears that two groups are created one larger, that is catering to maximising the surface area fitness criteria and the second, smaller that is mainly catering to the minimsing volume fitness criteria. Morphological Observations In this generation, two groups of phenotypes are continuing to be created in spite of the chnages mde to the breeding strategy to include more variation. Morphologically, the array genes are seen to be most dominant once again. Emergent Technologies & Design | 16 AA School of Architecture

E DF A F CA E A F D F D E A A E

C F

D F C F F B BF C A A D E A AC A E E A D D E FD FA B

B CF

A A B AA D F A A D FE F C D BA B CC AD BB C EA F AE B B C A AC CA E A B F AB A DA F D BC B A DF BCB AFC DFA EB AA B E B D D DBBE AAA C A BFE AFA DF E B A A D B A AB C CA DA CE

C A

D CF F F F B C A A E A F A F C E D

B B D A E C BA F A F A F E

B F

A FB D CA A DC C D B C A

A BF BA C B CA A FD FD B AA E A A AB C D C B A AB E BE A B A E C ABBD AAC D C A E B A A F B A AB BA CC AD C BAD AC

C C

C B

B DB E A A C F F B C A F F F D C

FF B

C B AB F AA A EB B AC BA A C D B A B FE A FA D D C C B AA C B AB DA AA CC FB FA BCD ADA CCC DB CA B A A E A ABBE AAA FB BAC ABA C D B C A A B E AB A CA FD FD

C C

C B

BF A A E A C D BB BA BDC AEF AAF EBF AA F D F C D FC FF B Sequence I| Generation VII F B C A D D BC A A C C D C F A CA A F A F E B BD AE B

C A C 6 D BA AC A C C D B B A B A A E C A F B F A D Generation BB AA BAF AEA BAB CB AA C D CBB AA AC BEF AAF D D C

A B A A E F BA A A B B C A A C C D C DC A F BC AD B

F CB DA CD A C B A A B E C F F FF B D

Generation 7 B A C F F B AB DA AA CC C G B A D C C

A C C7 B A Generation Generation A AB EA AF A B B A AB EA AC F F B A6 D A B C A A C C B A C F F Generation 7 Generation 6 B C A A D B A A C B A B D C C D F C D C B A BA AE F A A B B A B A A E CA F F B A D BA AC A C C B A C F G B A E B Generation A A C B A EG DB DA DD C C 7 C Generation 6 B A A E A B A D A C B A BC AF AF C C G B A D C C Generation 7 Generation 6 B E A A F A B A A B A B A A E C A F F B A D AB CA A C C B A C F F G EB D D D C C A D G B E D A C C7 B A Generation B A G B AG EB DA DE DD D D neration 6 GD BD A D C C G B A E D D B Generation A A C7 C A B E D Generation 6 B A D A C B A A E A B A C F F G D C C B A B A D D D D B A EG DB DE D D D E G B A E C A B E D D D B A A Generation C C B E D D G A 7 AD E D D DG B E D D D A DD DC C B AG EB DG.7.3 G B A E GD BDG.7.4 C C 7G.7.2 G.7.1 B A AGeneration G.7.5 neration 6

B A E D GD B D A B A A Generation C C 7

7 123.2 m

C G B

C B A

A B D D3 E D G B A E G D5B D 622A m B A E DG DB DA D CG CB

A GE B D A D E D D D D B G E GD BD AD D C C

G EB D D D D E D A BG AB EA DE DD DD B

D A

C D

E D

E 3736.6 D D D E GD B D10 m3 D A GE BD AD ED D D

B D A 7EE 1419.5 A AC Em3 AG EB DE DD D D B

A AEm3 D B A AB6 E971.2 A D E BA A E C

D E

B B

B AA EE AD A AB DA EE AC

D E

G B AA AB CA CD A E BA A E D

E D

2 407.8 m

A D BE AA E C E B D B A E D GD B E D D G B A EG DB DA DE DB DA DE GD BD E D D

B DB

5 156.5 m

B A DB EA AE E D GD B E

4 226.3 m

F D

B A E D E C E

B Am3 D A9 3471 A E D D

B A C D C A D E A

C C

1 420.45 m

A BE AA E D D D BE AA E C E

G B

E D C A AB AA C B AB AA EE AD D

A G

B A CB DA CC CD A A AC BC A D

C E

C A

G B A A C C D C C

G B A A E BA A E D D B A D EB AA E C EB

B A

A AA BC AC D E A A BE AA E D D

B G

D C C B C C C A D A BA AB AA CD CE

C AB

B A CG DB CA C D BC AC C

A D

C C

B A A C C A A C C

B G B A A AC BC A D E B A A EB AA E D DG

A B

C B D A C CC D C C A AA BC AC D E A

B B

G C B D A C A C A AB CA DC CD CC

C C B

G B AB DA CA CC C D GC B A A

C C

F B A A A C C

A B C B A C D B C AG.7.6 C C D C G B A AA CB CA D EB AA

A C C C GD B C AG.7.7 C BD AC CC D C C

G B

C B AA G.7.8 C C A C B D AG CB DA CA C

C G

F BG.7.9 A A D BC AC A C

A C

C F

2 A Am C G C B B A C D G C B3 407.8 D mC3 B CA B A C DB CA 8 CC 3119.1

ACm C C A B D A8C 121 3 A mA C 4C 600 C GD B

2 F B9 92 Am A A 2 387.3 BB AA DA Cm C 3 CC

G B A D BC AC A C C B A C DG CB A A CG CB

F B A A A C A BD AC AC C C

F B A Surface A C Area C FC.IA max F DB CA CA C C

along a vector B

FC.II min Overall Volume

C F

Observations

Due to our revised breeding strategy and in specific, revised mutation strategy, new phenotypes appear in this generation. The issuu of repition of the ‘BA’ genome was tackled due to the introduction of a random gene in the first gene of thirty percent of the individuals in the generation this created great variation. Once again, within the ranking for fitness criteria the best phenotype is also one of the worst for the other fitness criteria, suggesting the fitness criteria are conflicting. With evolution of every generation two groups again become more prominent. With every generation further evolved it appears that two extreme groups are created however at this generation variated individuals seem to work for both criteria. Morphological Observations The array gene is most prominent morpholigically due to the mutation strategy of ‘duplication’ that existed from generation 1 to generation 5. New morphologies were expected to be created to move towrds an optimal solution., G.7.8, G.7.10 appear to be working towards the goal 17 |

Design Team 8 _ Mithuna Murugesh, Varvara Vasilatou Emergence 2016

10 67.2 m2 A3 A A m 1B 288.6

G.7.10 A

6 139.6 m 3 595.8 m3 2

D D D

B A B A D E BA A E C E B A E DG DB E D DB DA

A D

C A

A B

DB EA AA

EB AA

D F E BF A AC DD ACB C A

A B BA AF BA CB A

BF BE FC A FF D C AA E D B D B A FF E A AA E EC AD D B AF A EF F BD D AE C D B E AF A D BC CB A A C D B BD AE C A F BA F A F E A F B F A CBD DAA CCC DB CA A B E BA B A AA A FE B AAB

C C F AA F DF E F A B B A FC D FA E A B CC A DA CE F D F BC B B BD AE A A BE F A C C BD AA FAA F BC AD D F D D BC A C D C B B A A ABF EAA ABB CB AA C D B B CA A A CE B

B B B A BD CAE F AA FEFA A BA AE F BA A B C A FB B

FA B C A CC CD C DAD A A C F F B E A A

B Generation A B B C A C D C B A VIII Sequence CB FA A A A I| F B B A A C C C C B

A AC A E B A B D A CC AD B BB C BD F F BC AD C A A BF E AA C A C B A BA AE CA FB FA D D B A A C A C D B B

BA B

C C

G.8.3

C B

Generation 8 C C

A C

BGeneration A A 9C

Generation 8 G B A

A E

C D

BGeneration A A 9C C G B A EE DD DD

B G B

G BB AA EA DC DC E E D A D F G B A D

B A B

B D A D A D C C G BB EA E D D G FB GG E AA D DE DA D ED A BB

G BB AA EE DD DD D B D E D D D B A E

G G B

GG 376 BB mA2A DE ED AD F 3 A D A G B E E D D G F D 3

G.8.5

AE EED DD D D BA C GB AE GB B BA AEE ADD C DD C D B EA G BG BAB AEA ADE CDD CDD D B A EG D D B A D BA AB DD DE DA D D EA EA BDG ADB DA EE GADB D D BE AAG EB CA EE D B D A DBA FAE G A A C B C E

A E B D G D E A D D B AA EB DE DD DD D A D BF AG EB G B AA DA EC AC F A C ED C D D DG B D AD B GFA BB C AE

B ED GB D BA D AE DD ED A FG AE A D E AD D D D D D DD E EC BB BAA AE E D DB AG GEB BDE ADD ED DD DB A DG E AB BA AE B A AB EA DC A DC E D D G GB ED BC B EB D AE DDB DA D A BE AAF EG DB DA DG E B AAA BBA AAC EDAC D A DC A EC C AC G DFB B BA A AA C FA D G C A

EE E DD A DD DD A DB AA ABA C BG G AB CD B AB GDA BEE AAC E E D D B D A AF E AB BEA ADD EDE DG AA BB GG CA DA B EA D AD FCC BDG A AA BC CA DE ED ADB DA BC G B A D C D C BAA AAC DCC EC A D F C ECB D

DE DD GG C FB CD EA C BA C ED D BE B AD EDD D A B D C A B AB FAA GEE BAD AD D EG AB AB AA A C C B DA C AA BD BAC AAF C C BD AC CF DB CE CDB DA GC G AB C AA CD GCE B AAB BC C AC A A C G C A BA D G C C

D F AB D G D BB AA C E C A CA C G CB B GA C CC F EA A AC A CC A BA

A C

BB AA CA GF C G CB CA A A BF G AB B D CA E CA C BA AB AA G DD B A G BA BAB AAA ECD DCE DA DB A CG D C CE ADD ACD EC B DA DC BF C AB E AA G AA B CA A CC C C C BA AD BAC ACG C G D BC G AB AA CA CAG CB C B DA GA BD AC CC C C CCB C A A A C

B C

B AB GCA FDC BCD ECC DC DB A BD ACA ACB CA C C C

5 306.1 m

G BB A AA BDA ACC CCC C C B A A D C

F BB AA AA AA CC CC C

6 139.6 m

FA EBA G AA BAC AAC CC CC A B A A C C A DA GD BC AC C A

C C

C FB

BE1 480.3 AG ABm3AA CC CC A B A A C C

9 105.2 m

3 A C CF AC BB G AA4 B A C mAC C617.2 AA D C C B A AD DG CB CA C C

BG AB CA CA A C B C A C C G D D A A

C B

C C AE AG AB CA C A B A A C C

8 108.3 m 3 B C A G D mD A C 599 2

F E G

G D G C

E A B D

G.8.10

B C G AC E AA C A C C AA A A C C

A 3

BD AG AB DA CC CC

G.8.9

AA C BB CC GB FC BA BAA AAA DCC EC C AB AB D CA CA AAG C A A CG D G CB C F BB AA AAE AAG C CA C CB A D A G D B C A C C

ACA ACm3AC CG CB AG GCB BD 7A 2788.2 F BB AA AA AA CC CC

B C A C A E D G G D D B DF AB A A E C D D E B A D A C BG AF C B C G A E A A C B A C C B A BA A ACC ACC A C C A GB

G.8.8

G.8.7

G G B

G FG BB EA DA DE D D B CA CA A C C G A A B

A F B

10 3709.6 m

G.8.6

Genera B A

C D

A D

Generati

E BG AB4 344 EAB m EE DD DD CA D ED B D D E3 DD B 6 BE A2573.5 A AA A D mD

407.8 m B407.8 GB2 D AAmD EC DC D A D A1 D BA AC m EC D D G G EB10DA96.2 B E B AGeneration D A EG D B D 8DD DC C G B AB E Generation 9D AD A A D B9E DD 3E D AE BEA ACB EEA D3E D DBD DA FB B E G D BD B DAm3DE DDB DA BD G G2 E D A 3689.9 E G A GA m 8 3546.3 m 480.9 2

FD D C C F F

A D

G.8.4

A C C G D B D A D C C A D B Generation A A C B A Generation E D EB D G B AGGeneration 9E DDD DD D 7 C AE BD BED 8ADB EDA D DA DCB CA GB AC D EE D D B A G E BD DA E DA DC DCB D A GE G B A C D BA A E E B

A D D B A A C E F A F B A C F F

B A

AE A

G.8.2

G.8.1

D F

B A A E A B A BB CA AC D C A B B A A A D C C D C C B BA AA BCE AFA DF D C C C C C B A B D A A

Generation 7 Generation 6 A AA B C D C B A BA AE B FA BC B B CA FD FD C C B A D A B A C F F D AB CA A C C G B 8A D C C A C 7C B AGeneration Generation Generation 6 B A BF E AF A D D C C B AB DA AA CC C B B A A BA A BA BC A B A C F AE B A D A C B A C F F Generation 7 GenerationG6 B A D C C B B A A9 C E C D D G A Generation A E D 8D B Generation A C 7C B A Generation ion 6 B ABAAECAF F B A D BA AC A C C B A C F F G B 8 A D C C A C C B A Generation Generation 7 Generation 6 G A AGeneration C C 7 G BB B AA E AE D CD D CD D B E AA D CE D 9 CD D D G B BG A AGeneration B A GGeneration E BD AD 8DD C C B A D A C B A C F F B BAGeneration C D D C C B DA D AA EC 9D G B A AGeneration C C B A EGGeneration D Generation 6BD A DD C DD C AE ED8 A DD C D D B A GG G BB AA AED7C DE D D G A E G D Generation D D B BB E EEC D A AA E BB D B D A 8 C C G B A E D Generation 9

A F

B C

AD AG DB

7 136.8 m B m A3 A 5A 1166

FC.I max Surface Area along a vector FC.II min Overall Volume

Observations

In generation eight repitive patterns in the genomes start ocurring when an individual is working towards a specific criteria. For the fitness criteria of minimising volume, the genes of ‘AACC’ keep repeating, the phenotypes that do not have an array gene work towards the minimisng volume criteria. Wheres, for maximising surface area the repition of the same gene, ‘D’, ‘ED’, ‘DD’, ‘DDD’, ‘EDD’ is evident. The genes D and E are arrays genes the increase the size of the individual thus making it work for the maximising surface area criteria. Genome ‘G’ has a great impact on the population and is retained by the system whereas genome ‘F’ once again starts disapearing sinpite of its introduction through mutation. Once again, within the ranking for fitness criteria the best phenotype is also one of the worst for the other fitness criteria, suggesting the fitness criteria are conflicting. Morphological Observations The array gene is most prominent morpholigically once again. Two groups of individuals exist on on the smaller side and the other on the larger size which is clear morphologically in this generation. Emergent Technologies & Design | 18 AA School of Architecture

BF A A BA BDC AEF AAF E

AB FDC B BF C CF D FEA AA B FB FA BCD ADA CCC D

AC EAF B BB BBA D AEF AAF D A B A BB AA BAF AEA BAB C

AD D CA B DC AC A C F B FBA C B A BA AE CA FB FA

E A C

D F

E F

D D B A B E A

A B B C

A B

AB BA CC AD

FB FA

A F E FD A FE BB A EB AA DA C A C A A C B E A A B A A B FB B A

A D

C B

B B A A AA D CA C E DB C CA FD FD B E AB A A A

A C

C B

B A

A D

BB CA AC D C AB CA A C C

B B A A AA C BC A D FB B C B AB DA AA CC

C C

CB FA FD D C C B A C F F

A AB C E A FA D F D B A C F

C F

A A D

C A C

B D A

C D C

A C EIX C A D CA B FB A CA CA DB SequenceEC I|AF Generation C D

E D

A B D

C A

F B E

A F A

C BC A B AB AA F EB AA CB CA D BA AC

D A

C C

A A

B B

Generation 6 C EA F AD F BB ABA AAF EBA B ACB AA C D C BB AA AB A A C C B C F B A D A B A B A A Generation C C 7 eneration 6 C B BA AA CE BFA AF D D C CB A B D A A C B A C F F Generation 6 G B A D C C B Generation A A C7 C B A Generation E D D8 D B A D BA AC A C C B A C A C C B A Generation 7 neration 6 B AGGeneration EB DA 8 DD DC C G B A B EGAGeneration DBA DAC D 9EC D D B A C F F B8 DA DD C C A EG D B Generation B Generation A A C7 C D DC D DC E DA A EA AE D CD 9D CD BD A E G DGB DBB D G BG ABB EAAGeneration

B A

G B BGeneration AG A EB A DA9C DE C DD D D A G E B D8A D D D C C B Generation A EE A C AC D G EBB AAB BE D A CD E ED D AB D EA D D D BD D B AG EGB D

B BA AA9 EC D C D D G Generation E D DD D D AGB EBA CAE E

G B BB A EA A D D CD D E CD G AFG EGB DBA DAE DD ED A

G.9.3

BB AA EA DC DC BG AB DA EE AD

G A

BB AA EE DD DD D EA DB DE DD D D

F F

5B 388.8 m E ED AD AA D GG B BG 7EB DA DA 3 E D

G.9.7

C G C

A DC A CA A CCC A B A AB G BD AC C EC G G CBB DFAA CBA AC AB AC AG C C F BB AA AE C C CB A D A AG A C A G D B C A C B A

2 428.7 m2

CC 6CA CC2730.2 A A A A A BA DG A CB BB m3 C B C A C G C B A AD D BF

A AB EA C CC C A BC A AC A G A B A A C

7 139.6 m2

C C

B CB F E A AA A BG A4A 595.8 Cm A3CC CC C C A B A A C C B C

D A

G A

B D

A C

BA G AB B AA A C A C A C BD AG AB DA CC CC

D G

CC C FB E BA G AA B AA A AC C C A B A A C C

6 147.7 m2

BD 3 C CC C B D A 3G A 577 mA A

FC.II min Overall Volume A

Observations

Due to our revised breeding strategy and in specific, revised mutation strategy, new there is no repition of individual within the generation. However, there a few individuals are repeated from previous individuals..Once again, within the ranking for fitness criteria the best phenotype is also one of the worst for the other fitness criteria, suggesting the fitness criteria are conflicting. In this generation the phenotypes that work towards miximising surface area displaying exponentially larger values compared to the previous generations, indicating the gap within the two groups of indivudals is further widening. The evolution through more generations are required to obtain more accurate results. Morphological Observations Inspite of not having individuals repeating morpholigically G.9.2,G.9.3,G.9.4 and G.9,5 appear to be very similar.The array gene continues to be most prominent morpholigically. due to the breeding strategy that existed in G.1 to G.5. 19Â |

Design Team 8 _ Mithuna Murugesh, Varvara Vasilatou Emergence 2016

D F

Generation 9 B A E D

B B

A E

E D

D D

G B

B A

A A

E E

D D

D F

A B

B1 611.7 E Dm D A 10 D E A3

G B

B A

A A

E C

D C

FG BB EA DA DE D D D AG CB CA A A C C E

F G

B B

A D A C

E C

A E C A D A C BF AB A AE AG CB CA C C

G G

B B

A A

A C

A A

BG AB CA CA A C C AD DG CB CA C C

E B

G A

B A

A C C C C

C C AE AG AB CA C A B A A C C

AD AG DB

G B

G.9.8

FC.I max Surface Area along a vector A B A A C C BC AC C C

A C

D D G C B A

D D G CA C G F BB B AA A AAD CAE C C FC BBE AAG AAB AAA CCC CCC

B DF AC BD C AD C E BD ADG C D CAD B CFA DG C CBE G BB A AA ADA CEC CAC B AG A AB A AG C AA C C C CA A A CB F C C CB G C BB AA D CA BE C C C BA C AG

D EC DD E AC B AD BG G AF B C CA CA C AAG ABB CAA C

B A

C D

D D ED D AD D AB A BA B AE E D GF ABB AAA CAD CAE CA C D GD BD

C D

DE D D B A F AB G EA B AE A D E EC E A D DD E D B DBA EBAE AA B AG D CA D CE E DA B CCB B E E D AD CDG DBF CAG D EA D C E D A BD G A BA ABB AAA C

B DB A FB A G D CD D E D A D EC E ABA D EE DA GD B B AA A AB B D AD A EG AE A A AE CDG DG C D C BD D DB CB DFA ABA AAA C D C DC C EC A GA D FC C EB BG B AB C

A E

A ED DDE DA B BF G AG B EB A D AGG CFB DBA CEA CDE DD D GD B

A A

D B

3617.8 m

G.9.4

3247.2 m

AA EB DE DD DD AF AB AA CD CE

D A

9 97.5 m2 3 A mC 2 A488.9

G.9.5

G.9.10 B

8 108.3 m 5 599 m3

5494.8 m

G.9.9

Generation 9 F

G G

B B

D ED D D G E BB DBAE DAE D AD D CD D C B A GD B B EA A AEEE CD B AG D DE D ED D AD E CAB A G DB DAB A FA EGE ABG EE BAD ADB AAA CBA D D D AB

G.9.6

407.8 DD DD D BG B AB A EA4E E Em C BBA AAB DE 9E EDD ADD 3DD

A D CE D CD FD E CD A D CED E DD A CBD A D BD A G C BG A B CC A G B BA A AB AAE A CAC C G C BE C AA A DBB CAG C A A B D A D AF BA C B CC GB D A CA A B AG C

BGeneration A A 9C C B AB E A DE DD DD

D A

C C C D DC D D E D 420.4 m B E AA D10 E 9 D Dm D D B A G EG B D B A DE 3A D96.2 G B BG A A Generation B EB D DE E3 D A DA D EA GA EBE AAB E DE 3 D C B AB D AD A F EBG AAG m 8A 3471 1 480.9 m BD D

Generation 8 G B AB EA DA DC

Generation 8 Generation 7 C C B AB DA AA CC C B F F B A Generation A C 9 C G Generation B A D 8 C C C C B A C F F

Generation 7 BB AA EA G Generation 9 DC DC D F F G B 8 A D C C Generation D G BGB EBA DAA DEC DDC D

D C

G.9.2

G.9.1

B F

Sequence I| Generation X

Generation 10

G.10.1

9 108.3 m

A B A C C C B A A D E A G B A A E D G D G E D D

G.10.2

10 E B A A G Generation

Generation G B A10 m A3 2 599

G D

D E

A A

2 611.7 m

B A C C E m A10 D A3 5494.8

A D

E D

B G

A E

D D

E D

G E E D

1 468.3 m

A D

m D D G E G 4D 365.8 G B A E D3 D F

G B A A E D G D G E D D D

A A D E A G A E D D 2

7 3007.2 m

B B

A A

E B

D A

D C

F B A A A C G B B A C C

E A

D C

G B A E D D F B A B A C C

F G

B B

A B

A A

A C

C C

F G

B A A A C B B A C C

F D

B G

A B

B A

A C

C C

F D

B A B A C G B A C C

10 139.6 m 3 mC

F G

B B

A B A A3 C898.4 C

G.10.8

8 168.8 m

B4 1146 B Am3C

G.10.9

6 92 m 1 G437.3 A3 C B m

B A B A C G B A C C B

C C

G.10.10 D

7 97.5 m 5 1242 m3

G.10.5

G F

G.10.7

E G A E D D F B A A A C C

3 325.2 m D E A A A E 8G 3107 m3 D

B E

D D

A C

D D

A A

E D

G.10.4

D C

F B

A E

B D

D A

A G

G G

E A

G B A E D D F B A B A C C

C A

A A

D C

C E

G B

D C

C D

B A

E F

E A

A A

A B

G D G E D D G B A E D D

A B

G B B A

D D

F B A B A C D G B A C C

D D

E G A E D D F B A A A C C

G B 10 A Generation

G B A A 3E D E mD D G D 6 G2779

E E

E B

G A

F B A A mA3 C G B 9 B3617.2 A C C

Generation G B A10 A

D B

G D G E D D G B A E D D

5 407.8 m

G G

G D B A

B B

Generation 10 A G B A

B A A D E A E G A E D D

A B A C C C B A A D E A

B A A D E A E G A E D D

G.10.6

A E

G A A A

G.10.3

G B D G A A

Generation 10

FC.I max Surface Area along a vector FC.II min Overall Volume

Observations

The trend that has been seen over the last generation is most clear in generation 10. The existence of two groups is exxagerated in this generation. The phenotypes that work towards miximising surface area displaying exponentially larger values compared to the previous generations while the individuals that cater towards minimising volume shows a decrease in volume but not as large as the change in the individuals catering towards maximising surface area. The evolution through more generations are required to obtain more accurate results and a change in strategy is required once again.

Morphological Observations

G.10.2,G.10.4,G.10.5,G.10.6 have the genes ‘EDD’ which are the array genes that make them work towards the fitness criteria of maximisng volume.

Emergent Technologies & Design | 20 AA School of Architecture

GE NE T I C I NFORM ATION TABL E Generation 8

G.7

G.6

Generation Generation 10 10

Generation 9

G.8

Generation 7

Generation 6

G.9

G.10

POPULAT ION D I S T R I BUT I ON G. 6 -G. 1 0 FC.1 Standard Deviation Graph, G.6-G.10

0.004

0.00035

0.0035

FC.2 Standard Deviation Graph, G.6-G.10

0.0003

0.003

0.00025

0.0025

0.0002

0.002 0.00015

0.0015

0.0001

0.001

0.00005

0.0005 0 -400

-200

G.6: μ = 1462.26, SD = 1265.5 G.9: μ = 1716.5, SD = 2130 G.7 SD Steps G.10 SD Steps

21 |

200

400

G.7: μ = 1521.1, SD = 1299 G.10: μ = 2232.9, SD = 1551 G.8 SD Steps

600

800

G.8: μ = 1959.9, SD = 1346.1 G.6 SD Steps G.9 SD Steps

Design Team 8 _ Mithuna Murugesh, Varvara Vasilatou Emergence 2016

1000

0 -4000

-2000 G.6: μ = 1462.26, SD = 1265.5 G.9: μ = 1716.5, SD = 2130 G.7 SD Steps G.10 SD Steps

2000 G.7: μ = 1521.1, SD = 1299 G.10: μ = 2232.9, SD = 1551 G.8 SD Steps

4000

6000 G.8: μ = 1959.9, SD = 1346.1 G.6 SD Steps G.9 SD Steps

8000

4. Observations

For the generations G.1-G.5, the crossover logic was always taking into account the first 2 genes (40%) and the last 4 genes (40%) of the individuals for breeding, regardless the actual length of their genome that could have been augmented throughout the evolution. Therefore, the mutation of duplication that was introduced in the system didn’t have actual impact on the individuals and this is one of the primary reasons of producing twin phenotypes in each generation. The permutations and the combinations of genome sequences and limitations of its length show signs of a probable stagnancy throughout this experiment. The revision of our strategies for generations G.6-G.10, on the other hand, resulted to a great variation in the genome configuration of the phenotypes as shown in the table. Gene G is accepted and incorporated into the system, whereas gene F still comes in extiction even after introducing in through mutation. It is evident from the data that the gene pool needed differantiation and maybe expansion in terms of intensity and count to achieve more variation. The genes selected should affect both fitness criteria in order for the experiment to produce more valid results. The graphs suggest that the populations decrease their standard deviation in both fitness criteria, which indicate an increase in their variation throughout the experiment. Both volume and are means are increased, despite the introduction of a new gene and the differantiation in the breeding strategy. Nevertheless, the extent of the experiment (just 10 generations of 10 individuals), it is relatively difficult to permutate and combine various breeding strategies in only 10 generations which increases the similitude in the deviation factor and ultimate results.

Observations & Conclusions Conclusions

The first sequence evolved the creation of population of individuals in an “analogical” way, by 3D modelling in Rhino environment. Simple geometrical operations were applied to a basic primitive and a maximum of 2 criteria were defined. The implementation of these simple actions and strategies allowed us to have greater control over this simplified evolutionary process. It was possible to easily manipulate the desired outcomes through the design of clear breeding and crossover strategies. lt was quickly evidenced how the body plan allows for much higher variation in correlation to the genes that act upon it, even with a simple tool kit (small genepool), and short genome structures. In this Sequence it was possible to predict to an extent and manipulate the outcomes through the strategies defined. Through analyzing the resulting phenotypes and their genomes respectively, it was possible to detect similarities in between the individuals, recognize certain patterns such as the approximation in volume size as well as the arrangement and organization of their entire morphology. Furthermore, with the introduction of mutation operations it was possible to detect ‘desirable’ genes and introduce them in a particular position that could ensure the gain in the fitness of the individual. These empirical simplified experiments helped gain a basic understanding of evolutionary algorithms and how to think about the design of strategies for evolutionary processes.

An attempt is made to inculcate all the above in the next sequence to avoid similar inferences and observations.

Emergent Technologies & Design | 22 AA School of Architecture

Sequence

25Â |

Design Team 8 _ Mithuna Murugesh, Varvara Vasilatou Emergence 2016

7. Sequence II| Shibam, Yemen Deep in Yemen’s most remote valley lies the city of Shibam. Surrounded by palm groves, and flanked by the steep cliffs leading up to the Yemeni highland on both sides, the city of 2,000 inhabitants hardly seems impressive. Just a handful of high-rise residential buildings, not so different from the Soviet-style blocks found across the Arab world.Yet these buildings don’t date from the 20th century, or even the late 19th century. They were built almost five centuries ago, and have remained largely unchanged since.

“Sometimes called the ‘Chicago of the desert’ or the ‘Manhattan of the desert’, the old city of Shibam presents to historians and urbanists one of the earliest and most perfect examples of rigorous planning based on the principle of vertical construction,” UNESCO brief on Shibam.

Credited as the home of the first highrise apartment buildings, Shibam has become a symbol for the rise and resilience of middle eastern culture in the desolation of the surrounding desert.

Shibam is known as the first city on earth with a vertical masterplan. A protected UNESCO World Heritage Site since 1982, the city is home to densely packed buildings ranging from four to eight storeys, beginning in 300 AD but now mostly built after 1532. Thanks to a fortified ring wall, the city has survived nearly two thousand years despite its precarious position adjacent to the wadi floodplain. Mudbrick has a higher heat capacity and lower conductivity than concrete, which means it slows the rate at which the temperature within the building changes. It’s also cheap to produce - labour costs are the only real costs involved - and it’s eco-friendly. Not only does the production of sun-dried bricks involve no polluting emissions, the bricks are also reusable. And, unlike fired bricks, the physical structure of dried bricks does not change during the drying process. Without its white protective layer, a wet brick simply becomes mud. In Shibam, climatic considerations manifest themselves in more than just the building material. Wooden windows provide privacy, refract glare and promote air circulation with their low placement, and small ventilation holes near the ceiling. Narrow streets and open plazas further enhance this air circulation on a city level. Thus, the architecture of Shibam reveals a complete approach to urban planning, fine-tuned to the city’s climate and social structure. There are data on its existence since the second century BC. It was on numerous occasions the capital of the kingdom of Hadramaut. Its architecture with multi-storey buildings, adobe, separated by a labyrinth of narrow alleys, is worth the nickname of Manhattan of the desert or “the oldest city of skyscrapers in the world.” The method of construction of buildings on raw land is ancestral, the oldest being preserved from the sixteenth century. The city is the oldest example of urban planning based on the principles of vertical construction, a well-defined plan. The highest buildings reach 16 floors and a height of up to 40 meters, with the minaret, with 50 meters, the tallest building in the city. The city contains numerous public squares , in an intertwined netwokr of narrow streets and main and secondary public markets and squares.

Emergent Technologies & Design | 26 AA School of Architecture

Sequence II| Shibam - Yemen

B. BO DY PLAN

30 m

5 cells

Gene Pool SuperC. block

5 cells

C. G E N E POOL Population of Courtyard’s Centre Population of Centre of Courtyard

Offset each Blocks’ boundaries Offset factor domain: 0.75 - 3.00

Scale of each floor of each building on X axis Scale non-uniformly on X axis

_702

Scale ofScale eachnon-uniformly floor of each building on Y axison Y axis

_702

Linear Array on Z axis - Number of floors/ building Linear Array on Z axis

_96

inner rectangular: 60 x 40

factor domain: 0.75 - 3.00

factor domain: 0.8-1.20

factor domain: 5-10

Gene Count:

27 |

Design Team 8 _ Mithuna Murugesh, Varvara Vasilatou Emergence 2016

_1503

7. Sequence II| Strategy Introduction

GenePool

The gene pool is devised in coherence with the fitness criteria and structure of the body plan. The primary modifiers in this sequence are ‘offset’, ‘scale in Z’, ‘scale in xy’. A specific domain and gene count is assigned to each gene pool and a strategy to act on different body parts is made. A more detailed account of the same is given below:

•

Offset

A domain of 00 to 00 is set for the offset modifier with a gene count of 00 genes. The offset mainly focuses on increasing the width of the linear divisions between the body parts in the urban block i.e. the main streets.

Scale in Z

A domain of 5 to 10 is set for the scale modifiers with a gene count of 000 genes. Each gene acts on a single unit for the extrusion (building level) of the building within each body part. The numeric value of the genes in each gene pool is randomised to 100% before the first simulation.

Scale in X & Y

A domain of 0.8 to 1.2 is set for scaling in x & y axis modifier. Each gene acts upon a single block of the building scaling it up or down randomly to create variation within each block of the building.

The purpose of sequence 2 is to explore the potential of Octopus plugin with Rhino to simulate a genetic experiment for the evolution of urban blocks. In this part of the sequence, the primitive is changed from an abstract geometry (cube) to an urban block. The high density settlement of Shibam,Yemen is taken as a case study, and used as a new primitive for this section. Meanwhile a body plan is introduced for the primitive with a subdivision into 4 body parts: each plot around the Main Street intersection. Octopus/Gh is utilised as the evolutionary solver for the generation of new populations. Different pararmeters that affect certain body parts are defined and now build up the extensive gene pool, containing over 0000 genes.

Body plan

As a part of our experiments, sequence 2 basically emphasised on testing the evolution of this configuration as an urban block with multiple fitness criteria. The evaluation strictly pertains to a criteria based on an external environmental pressures in addition to other fitness criteria that are physically explicit int he architectural realm. The block footprint is treated as the initial blueprint of the body plan. It is 4 segments namely A,B,C,D where they all are the exact same in terms of geometry in plan. As the cell density of Shibam was extremely high and was mainly residential the body plan is so divided according to areas of each cell, which is uniform. The body plan is a relatively uncomplicated intervention and can be implemented in any other urban block of similar density.

Emergent Technologies & Design | 28 AA School of Architecture

FI TNESS CRITERIA FITNESS CRITERION

MAX OVERALL VOLUME

FITNESS MIN CRITERION SURFACE AREA 0 3 EXPOSURE

29 |

Design Team 8 _ Mithuna Murugesh, Varvara Vasilatou Emergence 2016

FITNESS CRITERION

MIN BUILDINGS’ FOOTPRINT

MIN GROUNDLEVEL EXPOSURE

7. Sequence II| Strategy Fitness Criteria

To iterate the generations in this seuence, the idea of multi objective optimisation is adopted where the fitness criteria was revised to establish a relation with the external changes in the environment. Since the research focuses on testing the evolution of urban blocks and forms within a specific climatic context, it is necessary to clearly define and ensure that the fitness criteria are relevant and measurable.

Fitness Criterion I

The first fitness criteria aims at maximising the total volume of the individual. It is defined as such to integrate the relationship between total massing of the individual and the internal spatial area available to maximise its extent. A total of 00 gene pools are assigned to achieve optimum fitness.

Fitness Criterion II

The second fitness criteria is to minimise building footprint, which contradicts the first criteria of maximising overall volume. This criteria aims at creating internal open spaces begeeen the dense packing of the cells and is controlled by 00 genes. The fittest individual to achieve this criteria would be the one with maximum offset value of 0.2 and minimum scale value of 1.2 respectively. Fitness Criterion III The third fitness criteria is to minimise the building surface exposure with respect to a single solar vector. The vector is limited to one point to achieve uniformity in comparing the results. This criteria is calculated on the basis of the surface faces area exposed to the vector. Fitness Criterion IV The fourth fitness criteria is to minimise the ground level exposure with respect to a single solar vector. This criteria works in contradiction to fitness criteria 2 of minimising the building footprint and thus, maximising the ground area. The vector is limited to one point to achieve uniformity in comparing the results. This criteria is calculated on the basis of the ground surface area exposed to the vector and not shaded from the shade of the buildings.

Emergent Technologies & Design Â | 30 AA School of Architecture

7. Sequence II| Shibam - Yemen

M OD EL SET-U P

31Â |

iii

Design Team 8 _ Mithuna Murugesh, Varvara Vasilatou Emergence 2016

PSEUDOCODE

Superblock Script

Parametres • • • • •

P: Centre of the courtyard D: Blocks’ boundaries N: Number of floors/ building X: Scale of each floor of each building on X axis Y: Scale of each floor of each building on Y axis

Constants

• Superblock boundaries, Dim: 120 x 100 (m) • Number of building blocks (4) • Number of buildings per block (25) • Height of each level, H (3.00m) • Number of building removed in the main courtyard area (4)

Pseudocode i

1. Create the Rectangular Base for superblock boundaries 2. Offset the rectangular base by 40 3. Populate a random point P inside the new rectangular

i i 4. Create Blocks’ Boundaries by the P and the rectangular base’s corners 5. Offset each boundary by random D values (domain: 0.75 - 3.5)

i i i 6. Divide each block into 25 cells

7. Define the four (4) closest cells to the P and delete them

i v 8. Copy and elevate each cell along the Z axis, N number of times by step H v 9. Scale each cell randomly along x and y axis non-uniformly by its centre, by X & Y values

v i 10. Extrude each cell by 3 on the Z axis

Emergent Technologies & Design | 32 AA School of Architecture

8. Sequence II| Generation III

G.3.1

4 10 1 3

211237 m3 10003.9 m2 115 m2 6654 m2

G.3.2

1 9 3 4

214800 m3 9497 m2 123 m2 6716 m2

G.3.3

5 8 2 1

G.3.4

209690 m3 9286 m2 117 m2 5548 m2

10 3 10 2

G.3.5

188550 m3 8929 m2 149 m2 6592 m2

2 2 4 8

212145 m3 8745 m2 137 m2 7114 m2

General

The simulation in Octopus commenced with the initial parameters set as shown in the figure on the right, to produce the generations G1-G3, with a population of 10 individuals per generation. Subsequently, the individuals in the third generation are evaluated and ranked according to our fitness criteria. For the first simulation, the default values of the set-up were used. The elitism rate refers to the percentage of the individuals that will remain unaltered for the next generation and is set at 50%. The mutation probability at 10%, with a rate of 50% and crossover rate to 80%. The simulation produced 10 phenotypes and they are ranked to facilitate uniform comparison with future generations. 33Â |

Design Team 8 _ Mithuna Murugesh, Varvara Vasilatou Emergence 2016

max Overall Volume min Building Footprint min Ground level Exposure min Buildingsâ€™ Surface Exposure

S i m u l a t i o n SET- UP Elitism Mutation probality Mutation rate Crossover rate

0.1

0.2

0.5

1.0

Sequence II| Generation III

G.3.6

7 1 9 9

192789 m3 8661 m2 147 m2 7557 m2

G.3.7

9 4 6 5

190879 m3 8983 m2 141 m2 6717 m2

G.3.9

G.3.8

3 6 7 7

211507 m3 9063 m2 141 m2 6919 m2

8 5 8 6

192445 m3 9049 m2 144 m2 6725 m2

G.3.10

6 7 5 10

205721 m3 9150 m2 138 m2 8202 m2

Observations

A great variation in the individuals cannot be seen. In plan, G.3.1, G.3.4, G.3.5, G.3.6, G.3.7, G.3.8, G.3.9 have nearly identical configuration with the central courtyard being at nearly the exact same point. The domain set for the position of the central courtyard may be limiting the development of varied configuration. The heights of the building in the phenotypes do not show differentiation in variation indicating once again that the domain for the building heights may have been limiting. The fittest individual for FCI is also one of the least fit individuals for FCII and vice versa which suggests the fitness criteria are conflicting. Emergent Technologies & Design Â | 34 AA School of Architecture

8. Sequence II| Generation III| Analysis

POPULAT ION D IS T R I BUT I ON

FC.1

FC.2

FC.1 Standard Deviation Graph, G.1-G.3

0.00006

0.0014

FC.2 Standard Deviation Graph, G.1-G.3

0.0012

0.00005

0.001

0.00004

0.0008 0.00003 0.0006 0.00002

0.0004

0.00001

0.0002

0 0.00E+00

5.00E+04

G.1: μ = 2.07E+05, SD = 9490

1.00E+05

1.50E+05

G.2: μ = 2.04E+05.2, SD = 8178.2

2.00E+05

2.50E+05

G.3: μ = 2.03E+, SD = 9933

C . 3 Graph, G.1-G.3 FC.3 StandardF Deviation

0.04

0 0.00E+00

0.0008 0.0007

0.03

0.0006

0.025

0.0005

0.02

0.0004

0.015

0.0003

0.01

0.0002

0.005

0.0001

5.00E+01

G.1: μ = 1.36E+02, SD = 23.11

1.00E+02

1.50E+02

G.2: μ = 11.40E+02, SD = 13.2

2.00E+02

2.50E+02

G.3: μ = 1.35E+02, SD = 11.72

Analysis & Observations

The initial set up from G1 to G3 is run using the default settings of Octopus. The standard deviation graphs indicate there is a convergence in all four criteria in G3. FC.1, FC.2 and FC 3 and FC.4 indicates minimal decrease in its value. FC.1 and FC.2 show slight convergence while FC.3 and FC.4 show great convergence. The ground level exposure and surface area exposure show great convergence which is due to the lack of variation in the individuals due to convergence in the volume and building footprint fitness criteria. Due to lack of variation in the phenotypes the results of building exposure and ground surface exposure show nearly similar values in all phenotypes, thus displaying the great convergence in the standard deviation graphs.

35 |

Design Team 8 _ Mithuna Murugesh, Varvara Vasilatou Emergence 2016

4.00E+03

G.1: μ = 9197.84, SD = 340.65

0.035

0 0.00E+00

2.00E+03

0 0.00E+00

6.00E+03

8.00E+03

G.2: μ = 9143.5, SD = 364.1

1.00E+04

1.20E+04

G.3: μ = 9137, SD = 369

F C . 4Graph, G.1-G.3 FC.4 Standard Deviation

2.00E+03

4.00E+03

G.1: μ = 6.98Ε+03, SD = 920.64

6.00E+03

8.00E+03

G.2: μ = 1205.2, SD = 478.9

1.00E+04

1.20E+04

G.3: μ = 1367.2, SD = 772

At this initial stage the aim would be to create maximum divergence of individuals before later converging to the most optimal solution. However, this early convergence is not desirable. Both the convergence in the individuals’ fitness value graph and the early convergence depicted in the G3 suggest to run the next part of the simulation with the aim of increasing the variation in order to locate other optimal solutions.

The term convergence can be defined as the point from where no efficient progress is evident in the values of the fitness assigned to a specific population over consecutive successions. It basically denotes a possibility of neutrality in the fitness values and decreases the probably of producing fitter individuals

Generation III| Analysis

Elitism Mutation probality Mutation rate Crossover rate

0.1

0.2

0.5

1.0

Analysis & Observations The 1st Delaunay graph (G.3) indicates that the is a general tendency of a linearity among the results, moving from top left to bottom right. This observation implies all our fitness criteria do not have equal weightage. In order to tackle this phenomenon, we try to increase variation by increasing mutation probabilty, mutation rate and reducing the elitism as this is the first stage of the simulation adn would like more varoiation at this point. Emergent Technologies & Design Â | 36 AA School of Architecture

9. Sequence II| Generation VI

G.6.1

2 10 7 7

211631 m3 9720.7 m2 138 m2 7530 m2

G.6.2

5 6 5 5

210597 m3 9211.3 m2 136 m2 6950 m2

G.6.3

7 5 3 1

G.6.4

208000 m3 9112.3 m2 120 m2 5731 m2

8 8 2 2

205000m3 9344.5 m2 115 m2 6051 m2

General

On the basis of observations deduced from the previous analysis, the mutation probability is set to 30% from 10% and the elitism is reduced to 20% from 50% to induce variation in the phenotypes. The elitism and crossover rate are kept the same as the previous simulation, as changing too many factors would not allow a clear observation to be deduced. Similar methodology of ranking the individuals Is adopted to test the survival of the fittest phenotype. The simulation is limited to three generation in this experiment G4,G5,G6 and is analysed at generation 6.

37Â |

Design Team 8 _ Mithuna Murugesh, Varvara Vasilatou Emergence 2016

G.6.5

6 7 4 6

210231 m3 9304.4 m2 121 m2 6972 m2

max Overall Volume min Building Footprint min Ground level Exposure min Buildingsâ€™ Surface Exposure

S i m u l a t i o n SET- UP Elitism Mutation probality Mutation rate Crossover rate

0.1

0.2

0.5

1.0

Sequence II| Generation VI

G.6.6

10 2 1 10

197725.8 m3 8856.7 m2 109 m2 7655 m2

Observations

G.6.7

211030 m3 8951 m2 10 147 m2 3 6072 m2 4 3

G.6.9

G.6.8

3 9 8 9

211433.7 m3 9721 m2 138 m2 7520 m2

In terms of plan, there is a slight increase in variation in the position of the main courtyard. However, G.6.3, G.6.4, G.6.5 and G.6.7, G.6.8, G.6.9, G.6.10still have nearly the same ground configuration. Though there is a slight increase in variation the variations in the position of the courtyard, morphologically the variation is not very clear further indicating the domain the position of the courtyard greatly limits variation in the phenotypes. The fact that there has been an increase in variation after changing the breeding strategy indicates the phenotypes do move towards variation but the model set up does not allow it.

9 4 9 8

204478.2 m3 9026.3 m2 143 m2 7376 m2

G.6.10

1 1 6 4

213532.9 m3 8853 m2 137 m2 6523 m2

While there is evident variation in the ground configuration, the variation in the building heights does not appear to increase. The small domain of 5-11 storeys causes this lack of variation. For further experiments a larger domain must be created in order for the simulation to run through all possible optimal solutions. The fittest individual for FCI is also one of the least fit individuals for FCII and vice versa which suggests the fitness criteria are conflicting. Emergent Technologies & Design Â | 38 AA School of Architecture

9. Sequence II| Generation VI| Analysis

PO PULATION DISTRIBU TION

C . 1 Graph, G.1-G.6 FC.1 StandardF Deviation

0.0001

0.0016

0.00009

0.0014

0.00008

0.0012

0.00007

0.001

0.00006 0.00005

0.0008

0.00004

0.0006

0.00003

0.0004

0.00002

0.0002

0.00001 0 0.00E+00

5.00E+04

1.00E+05

1.50E+05

2.00E+05

2.50E+05

0 0.00E+00

4.00E+03

6.00E+03

8.00E+03

1.00E+04

G.2: μ = 2.04E+05.2, SD = 8178.2

G.1: μ = 9197.84, SD = 340.65

G.2: μ = 9143.5, SD = 364.1

G.3: μ = 2.03E+05, SD = 9933

G.4: μ = 2.04E+05, SD = 11087.9

G.3: μ = 9137, SD = 369

G.4: μ = 9105.38, SD = 296

G.5: μ = 2.04E+05.2, SD =7279.95

G.6: μ = 2.08Ε+05, SD = 4480

G.5: μ = 9133.5, SD = 268.97

G.6: μ = 9209.3, SD = 300.3

0.0008

0.035

0.0007

0.03

0.0006

0.025

0.0005

0.02

0.0004

0.015

0.0003

0.01

0.0002

0.005

0.0001

0 0.00E+00

2.00E+03

G.1: μ = 2.07E+05, SD = 9490

C . 3 Graph, G.1-G.6 FC.3 StandardF Deviation

0.04

F C . 2 Graph, G.1-G.6 FC.2 Standard Deviation

5.00E+01

1.00E+02

1.50E+02

2.00E+02

2.50E+02

0 0.00E+00

F C . 4Graph, G.1-G.6 FC.4 Standard Deviation

2.00E+03

4.00E+03

6.00E+03

8.00E+03

1.00E+04

G.1: μ = 1.36E+02, SD = 23.11

G.2: μ = 1.40E+05, SD = 13.18

G.1: μ = 6.98Ε+03, SD = 920.65

G.2: μ = 6.87Ε+03, SD = 531.65

G.3: μ = 1.35Ε+02 SD = 1172

G.4: μ = 1.31Ε+02, SD = 14.77

G.3: μ = 6.87Ε+03, SD = 651.74

G.4: μ = 6.47Ε+03, SD = 574.81

G.5: μ = 1.27Ε+02, SD = 15.81

G.6: μ = 1.30Ε+02, SD = 12.31

G.5: μ = 6.60Ε+03, SD = 702.12

G.6: μ = 6.84Ε+03, SD = 667.48

Analysis & Observations

1.20E+04

The standard deviation graphs of the population distributions depict a general tendency towards convergence, which was not as expected. The FC.1 and FC.3 graphs present a sudden increase from G.3 to G.6, whereas the FC.4 and FC 2 display an increase in convergence, however not as drastic as FC.1 and FC.3.

the possibility to create more variation while also producing a pattern of phenotypes closer to the optimal solution.

The FC of ground level exposure and building height would expect to produce more uniform configuration of the superblock’s global geometry. However, the simulation’s results do not present a clear pattern of the building, due to the limiting domain of the building heights and the set-up of the script, which scales each individual level of the building. Therefore, for the next step, we could pursue a more abstract idea of the buildings, by creating one uniformly extruded building with a greater domain of heights. In this way, octopus could have

The morphological configuration of the phenotypes indicates slight variations with dominant patterns that could be analysed or further pattern that could be observed. This suggests that the definition’s domains should be revised in terms of range. However, the greatest issue at this point is the simulations results giving the complete opposite results than expected. Even though mutation rate was increased to create more variation a clear convergence can be seen in the standard deviation graphs which does not follow our strategy.

39 |

Design Team 8 _ Mithuna Murugesh, Varvara Vasilatou Emergence 2016

It is worth mentioning that the mean values of each fitness criterion are approaching the objective, even though there is an unexpected convergence.

Generation VI| Analysis

Elitism Mutation probality Mutation rate Crossover rate

0.1

0.2

0.5

1.0

Analysis & Observations

The 2nd Delaunay graph (G.6) indicates that the is a general tendency of a linearity among the results, moving from central top to bottom left. The convergence graph indicates there is a convergence in all four fitness criteria which is the opposed completely to our predictions. In this sequence, G3-G6, the breeding strategy was altered drastically to produce more variation however, the convergence graph indicates that instead of divergence, convergence was achieved once again. Emergent Technologies & Design Â | 40 AA School of Architecture

10. Sequence II| Generation X

G.10.1

3 9 10 3

207000 m3 9204.8 m2 138 m2 7530 m2

G.10.2

8 1 4 2

201000 m3 8714.5 m2 142 m2 6160 m2

G.10.4

G.10.3

6 7 6 9

205000 m3 9051.3 m2 131 m2 6957 m2

10 10 9 4

G.10.5

19500 m3 9500 m2 141 m2 6170 m2

2 4 2 8

209909.3 m3 8953.3 m2 121 m2 6789 m2

General

On the basis of observations deduced from the previous analysis of G6, to test the possibility of variation in the progression, the breeding strategy is redefined with an increase in the mutation rate and probability to 80% and 70% respectively. The hope is to create more variation which was expected to happen in G6 with the increase in mutation however did not take place, thus the mutation rate and mutation probability is further increased. Similar methodology of ranking the individuals Is adopted to test the survival of the fittest phenotype. The simulation ran for four more generations till G10 and is then evaluated.

41Â |

Design Team 8 _ Mithuna Murugesh, Varvara Vasilatou Emergence 2016

max Overall Volume min Building Footprint min Ground level Exposure min Buildingsâ€™ Surface Exposure

S i m u l a t i o n SET- UP Elitism Mutation probality Mutation rate Crossover rate

0.1

0.2

0.5

1.0

Sequence II| Generation X

G.10.6

5 5 3 5

205567.4 m3 8979.1 m2 123 m2 6341 m2

G.10.7

9 2 5 6

200011.5 m3 8881.4 m2 128 m2 6507 m2

G.10.9

G.10.8

4 6 8 7

206290.5 m3 9020.8 m2 137 m2 6533 m2

1 8 1 1

211142 m3 9110.2 m2 115 m2 5859 m2

G.10.10

7 3 7 10

202070 m3 8894.9 m2 131 m2 7400 m2

Observations

In plan, three groups if similar configuration have been created. G.10.1, G.10.6 and G.10.2, G.10.4, G.10.8, G.10.9 and G.10.3, G.10.10 have nearly similar positions of the courtyard. Despite using a strategy to increase variation there is no great variation in the individuals. All the individuals appear to be nearly identical in terms of building heights. They all appear to cater towards the fitness criteria in nearly the exact configuration of heights. G.10.3 is the fittest for both, ground level exposure and surface area exposure. No clear observations can be deduced due the results in this sequence being opposite to the predicted results. A divergence was predicted to be seen at this point however a convergence is seen in the results.

Emergent Technologies & Design Â | 42 AA School of Architecture

10. Sequence II| Generation X| Analysis

POPULAT ION D IS T R I BUT I ON

FC.1

FC.2

FC.1 Standard Deviation Graph, G.3,G.6, G.10

0.0001

FC.2 Standard Deviation Graph, G.3,G.6, G.10

0.0025

0.00009 0.002

0.00008 0.00007

0.0015

0.00006 0.00005

0.001

0.00004 0.00003

0.0005

0.00002 0.00001 0 0.00E+00

5.00E+04

1.50E+05

1.00E+05

G.3: μ = 2.03E+, SD = 9933

G.6: μ = 2.08Ε+05, SD = 4480

2.00E+05

2.50E+05

FC.3

0.001

0.045

0.0009

0.04

0.0008

0.035

0.0007

0.03

0.0006

0.025

0.0005

0.02

0.0004

0.015

0.0003

0.01

0.0002

0.005

0.0001

0 0.00E+00

2.00E+01

4.00E+01

6.00E+01

G.3: μ = 1.35Ε+02 SD = 1172

8.00E+01

1.00E+02

1.20E+02

G.6: μ = 1.30Ε+02, SD = 12.31

1.40E+02

1.60E+02

1.80E+02

G.10: μ = 1.29Ε+02, SD = 8.34

Analysis & Observations

The breeding strategy undertaken for this generation was aiming into achieving a divergence to all fitness criteria. However, the graphs suggest a clear convergence towards all the fitness criteria. The analysis conducted to all the generations until the last one showed that there was presented convergence in earlier generations before concluding again to convergence in spite of changing the simulations strategy to attain divergence. Despite constantly altering the simulations strategies to attain variation only convergence is created. This phenomenon can be explained by the fact that either our fitness criteria are not very conflicting to allow the Octopus define an optimal solution, or our breeding strategies were not aggressive enough (however, this seems unlikely as the mutation rate and mutation probability were set to 60% and 70% respectively) or there is an error in the code, which seems like the most likely reason. 43 |

Design Team 8 _ Mithuna Murugesh, Varvara Vasilatou Emergence 2016

2.00E+03

4.00E+03

G.3: μ = 9137, SD = 369

G.10: μ = 2.04Ε+05, SD = 4650.3

FC.3 Standard Deviation Graph, G.3,G.6, G.10

0.05

0 0.00E+00

6.00E+03

8.00E+03

G.6: μ = 9209.3, SD = 300.3

1.00E+04

1.20E+04

G.10: μ = 9030.98, SD = 201.62

F C . 4Graph, G.3,G.6, G.10 FC.4 Standard Deviation

0 0.00E+00 1.00E+03 2.00E+03 3.00E+03 4.00E+03 5.00E+03 6.00E+03 7.00E+03 8.00E+03 9.00E+03 1.00E+04 G.3: μ = 6.87Ε+03, SD = 651.74

G.6: μ = 6.84Ε+03, SD = 667.48

G.10: μ = 6.47Ε+03, SD = 449.74

Generation X| Analysis

Elitism Mutation probality Mutation rate Crossover rate

0.1

0.2

0.5

1.0

Analysis & Observations

The 3rd Convergence graph (G.10) indicates that the is a general tendency of convergence once again. This results goes completely against the predicted result as the strategy for this sequence was altered to produce more variation. It is clear that there might be a problem with the code itself as the results have gone against the strategies predictions in spite of altering the strategy drastically to induce divergence. Emergent Technologies & Design Â | 44 AA School of Architecture

Generation X Generation VI Generation III 45Â |

Design Team 8 _ Mithuna Murugesh, Varvara Vasilatou Emergence 2016

11. The overall genes set created a very limiting set up of the model. Thus, for future investigations a more abstract model needs to be created, taking into account the main characteristics of the city but not every tiny inconclusive detail. A more abstract model would allow us to make more clear observations. Thus, from sequence two we were unable to make any observation from the graphs as there were several reasons for the results to be inconclusive. The sample of 10 generation was also not enough to have a clear understanding of the results displayed. Within Octopus, the graphs get scaled inside its environment thus, clear conclusion cannot be drawn from the fluctuations and convergences in the graphs.

Observations & Conclusions As an overall comparison, it is observed that at generations 3,6 and 10 the data suggest that we meet a convergence inspire of applying a logic to create more variation. Also, the overall fitness under each fitness criteria did not undergo a significant change. the fact that we met convergence even though we tried to variate and mutate, suggest that there is a problem in the code. The convergence in the graphs cannot be explained by the strategies that were undertaken, returning to the gh script it became clear that use of a ‘seed’ skewed the results. The use of a random number generator, ‘seed’, did not permit octopus to optimize the results towards our goal. It appears that our results reached a local peak by chance. The seed component did not allow octopus to reach the desired goal, as it didn’t have control on the numbers that were generated as the seed simply inputs a random number without taking into account the effect the previous imputed number had. The data suggested that our criteria was not in conflict however, this cannot be confirmed as the error in our code might have had an effect on the results. In order to make safe conclusions first, the seed in the code needs to be altered to a gene pools and the simulation must be run again and analysed. For future investigation, the body plan could be redefined to differentiate the four block as body parts with different genes to act upon them such that they future phenotypes could evolve in relation to each other and not as completely separate blocks. In terms of future investigations, a new relationship could be introduced in terms of proximity of the buildings to the main courtyards and its relation in terms of heights created or a new relation between the block could also be defined through the evolution of paths created. Significant exploration in terms of redefining the body plan and increasing the domain of numerical values in the gene pool may results in morphological variations as it was complex to record the evolution and quantity the date of each individual. A conclusion cannot be made from this sequence, due to various factors. One being the complexity of the model, in the scaling of each level of a building, not enough variation in building heights could not detect a pattern. The scaling factor created a block of every level of the building that was a different size which created great complexity that was unnecessary. Also, the range of storeys from the building was between 5 and 10 storeys which did not create enough variation in the heights of each block thus, most phenotypes look very alike and this limited the evolution of an optimal result.

Emergent Technologies & Design | 46 AA School of Architecture

Sequence

III

Superblock

III III

IV IV

IIII

Local networ k emergence

A p p r o a c h

III

iiiiii

iviv

iiii

240 m

Global networ k f or mation iiiiii

iviv

iiii

Pr i m a r y N etwo r k S eco n d a r y N etwo r k Ter ti a r y N etwo r k

Body plan

Superblock

III

A p p r o a c h

Local networ k f or mation

200 m

II II II

Body plan

200 m

IV IV IV

Through main courtyards’ positioning & interrelations emerges the local network of inner courtyards

iiiiii iii

iviv iv

iii

iiiiii

240 m

Global networ k emegence

Pr i m a r y N etwo r k S eco n d a r y N etwo r k Ter ti a r y N etwo r k

Through inner courtyards’ positioning & interrelations emerges the global network of main courtyards 49 |

Design Team 8 _ Mithuna Murugesh, Varvara Vasilatou Emergence 2016

12. Sequence III| Strategy Introduction

Sequence 3 consists an advancement from Sequence 2 by aggregating and combining the nominated urban block to formulate a superblock of 16 units. The model is simpliﬁed, through main principles’ abstraction, in order to reduce the computational load on the simulation and enable the extraction of more explicit observations. In this sequence, two experiments were conducted in conjunction to each other. Two approaches towards dealing with the network system of courtyards were undertaken, one (A) where the main courtyards’ positioning and interrelations were initially established and the local network configuration emerged and the second (B), where the secondary network was first created and the main network configuration was thus created. The aim of these experiments encompasses the identification and development of two different approaches in emerging a decentralized system aggregated in the urban context. The simulation is set to run a total of 100 generations with a population count of 50 individuals per generation. The phenotypes of generation 30, 60 and 100 are evaluated and breeding strategies are experimented with, accordingly. PRIMITIVE

In the previous sequence, the Body Plan comprised of four similar blocks with similar dimensions. However, in this sequence, the dimensions and the shape of the blocks and the blocks-units are neither similar in size nor in shape, due to the nature of the emergence. There is a primary network of streets, formulating four main blocks-units of different dimensions and values as it undergoes evolution from generation to generation. Each blocks-unit is consisted of four blocks, formulated by the secondary network of connections between the main courtyards. The tertiary netwrok is configured by the connections of the local courtyards to the main courtyard of each blocks-unit.

The main ambition in sequence 2 was to follow the characteristic morphology of the existing city Shibam and encourage the maximization in volume of individual cells. The same ambition is pursued in sequence 3, however, the characteristic of the city is altered slightly by introducing, open spaces, (courtyards) at defined intervals along the primary and secondary connectors (streets). The dimensions of these pockets are controlled via separate domains of different range in order to maintain the consistency in maximizing the building volume. All data is analyzed and documented on basis of its absolute values. The units of measurement are determining the divisions and extrusions are in meters for the sake of architectural relevance and calculation. The body plan is modiﬁed from the previous sequence, following the aggregation. It consists of four parts with each part including four plots within.

Emergent Technologies & Design | 50 AA School of Architecture

FITTEST

51Â |

Design Team 8 _ Mithuna Murugesh, Varvara Vasilatou Emergence 2016

LEAST FIT

12. Sequence III| Strategy

FITNESS CRITERION

MAX DENSITY BLOCK

The ﬁtness criteria for this sequence are similar to the criteria of the previous sequence but focus largely on additional parameters that conform to the superblock conﬁguration. The criteria have been retained unchanged for both experiments for the purpose of a validate comparison of the results.

Fitness Criterion I

This fitness criterion aims at maximising the ratio of the buildings’ volume per each block. It is defined as such to integrate the relationship between the total mass of the individual and the internal spatial area available to maximise its extent. A total of 00 genes are assigned to achieve the goal.

FITNESS CRITERION

MIN BUILDINGS’ FOOTPRINT

FITNESS MIN CRITERION GROUNDLEVEL EXPOSURE 03

FITNESS CRITERION

MIN BUILDINGS’ HEIGHT

Fitness Criterion II

Minimising buildings’ footprint aims at creating internal open spaces betweeen the dense packing of the cells, which contradicts to the criterion of maximising overall density. Buidings’ footprint is variated by 00 genes, that differantiate the size of the block, as well as the number of the buildings per block. The fittest individual to achieve this criteria would be the one with maximum offset value of 0.2 and minimum scale value of 1.2 respectively.

Fitness Criterion III The third assigned criterion refers to the minimisation of the ground level exposure with respect to a single solar vector, which works in contradiction to the criterion of minimising the buildings’ footprint and thus, maximising the ground area. The solar exposure is limited to one vector to achieve uniformity in comparing the results. The groundlevel exposure is calculated on the basis of the ground surface area exposed to the vector and not shaded from the shade of the buildings.

Fitness Criterion IV The minimisation of the buildings’ heights is introduced to the system as conflicting to both the criterion of maximising volume and minimizing groundlevel exposure. This criteria is calculated by the overall addition of every building’s height.

Emergent Technologies & Design | 52 AA School of Architecture

12. Sequence III| Model SET-UP Experiment A

I PSEUDOCODE

Superblock Script

range

III

Experiment A i. Generate a point/area of each quarter of superblock that define the main courtyards ii. Define the main courtyards’ connections iii. Retrieve the centres of blocks’ boundaries iv. Define the connections of the main courtyards and the blocks’ boundaries v. Define the inner courtyards’ position in the centre of the formulated blocks vi. Define the inner courtyards’ connections to the corresponding main courtyard

53 |

Design Team 8 _ Mithuna Murugesh, Varvara Vasilatou Emergence 2016

12. Sequence III| Model SET-UP Experiment B

Superblock Script

PSEUDOCODE

range

III

Experiment B i. Define the range of points’ moving along the boundaries of the superblock ii. Generate the points/edges of the main streets

iii. Create the main streets by connecting the points iv. Define the inner courtyards’ position in the centre of the formulated blocks v. Define the main courtyards’ position in the intersection of the formulated streets vi. Define the inner courtyards’ connections to the corresponding main courtyard

Emergent Technologies & Design | 54 AA School of Architecture

13. Sequence III| Generation XXX Experiment A

Block Density

Building Footprint

Ground exposure

G.30.28

G.30.41

G.30.15

FITTEST

291.8 m3 29033.2 m2 3728 m2 4068 m

G.30.3

LEAST FIT

281.7 m3 2996.5 m2 2873 m2 3941 m

237.2 m3 24969.6 m2 5071 m2 3476 m

G.30.15

201 m3 26732.1 m2 4965 m2 3137 m

G.30.22

281.7 m3 2996.5 m2 2873 m2 3941 m

230.9 m3 25606.6 m2 5266 m2 3033 m

Building Height

G.30.20

206.6 m3 25752.9 m2 5119 m2 3052 m

G.30.27

286.7 m3 27711.3 m2 4201 m2 4091 m

General

The numeric values of genes are randomized to 100% before commencing the simulation. The experiment is paused at generations G.30, G.60 and G.10 in order to analyse the results, compare them to its predecessors and eventually revise the setup for the continuance of the simulation. Initially all values of mutation, elitism and crossover are set to default as depicted in the diagram, and gradually adjusted accordingly to the analysis’ results and observations.

max Block Density min Building Footprint min Ground level Exposure min Buildings’ Height

Simulation Set-up Elitism Mutation probality Mutation rate Crossover rate

55 |

0.1

0.2

Observations 0.5

Design Team 8 _ Mithuna Murugesh, Varvara Vasilatou Emergence 2016

1.0

The individuals are ranked according to the value of ﬁtness. Generation 30 produced mild variations in the morphological configuration of the phenotypes in terms of plan, with evident fluctuations in the width of the primary and secondary street network. It is observed that once the positioning of the main courtyard adjoins the superblocks’ limits, the main street network almost disappears. There is a marked discrepancy in buildings’ heights’ configuration, though no specific pattern can be identified.

13. Sequence III| Generation XXX Experiment B

Block Density

FITTEST

Building Footprint

G.30.13

G.30.46

LEAST FIT

0.2

G.30.4

G.30.13

312.8 m3 20465.2 m2 6735 m2 11004 m

340 m3 28298.8 m2 2312 m2 14670 m

312.8 m3 20465.2m2 6735 m2 11004

G.30.24

G.30.47

G.30.31

G.30.46

284.6 m3 24577.9 m2 5003 m2 11988 m

365m3 2707.4 m2 2911 m2 14700 m

343.4 m3 2052 m2 6840 m2 11118 m

0.5

372 m3 28788.4 m2 3088 m2 16299

General

Simulation Set-up 0.1

Building Height

372 m3 28788.4 m2 3088 m2 16299

max Block Density min Building Footprint min Ground level Exposure min Buildingsâ€™ Height

Elitism Mutation probality Mutation rate Crossover rate

Ground exposure

1.0

The numeric values of genes are randomized to 100% before commencing the simulation.The breeding strategy is developed by pausing the simulation at specific generations from 0 to 100 and analyzing and comparing the output to its predecessor. Initially all values of mutation, elitism and crossover are set to default and gradually adjusted as per the analysis.

Observations

It is observed that generation 30 produced extremely different phenotypes in their morphological variation, in terms of plan, with each other and also in relation to experiment A. There is a large difference in their heights. Emergent Technologies & Design Â | 56 AA School of Architecture

FC.2 Building Footprint, G.10-G.30

0.0002 0.00035 0.00015

13. Sequence III| Generation XXX| Analysis & Observations 0.0003 0.0001

0.00025 0.00005

Experiment A

0.0002 0 0.00E+00 0.00015

5.00E+03

1.00E+04

1.50E+04

G.10

P o p u l a t i o n D i s t r i b0.0001 u t i o n G. 1 0 - G. 3 0 0.00005

FC.1 Block Density, G.10-G.30

0.00E+00 0.0008

0.025

0.0005 0.0009 0.0004

5.00E+01

1.00E+02

1.50E+02

2.00E+02

2.50E+02

G.20

G.10

0.005

3.00E+02

3.50E+02

G.30

0.0005 0 0.00E+00 0.0004 0.0003

G.10 G.20 G.30 1.00E+02 1.50E+02 2.00E+02 2.50E+02 3.00E+02 3.50E+02 σ : 15.87220977 σ2: 19.20711269 σ2: 24.97241203 μ: 2.58E+02 μ: 2.49E+02 μ: 2.51E+02 FC.2 G.10-G.30 G.10 Building Footprint, G.20 G.30 25.00E+01

0.00035

0.0002

2.50E+04

3.00E+04

3.50E+04

G.30

FC.3 Groundlevel Solar Exposure, G.10-G.30

1.00E+03

2.00E+03 G.10

G.10

3.00E+03

4.00E+03 G.20

5.00E+03

6.00E+03

7.00E+03

G.30

G.20

G.30

G.10

G.20

G.30

0.0012

0.00025

FC.2 Building Footprint, G.10-G.30

0.001

0.00035 0.0002

0.0018 0.0008

0.0003 0.00015

0.0016 0.0006

FC.4 Building Height, G.10-G.30

0.0014 0.0004

0.0001 0.00025

0.0012 0.0002

0.00005 0.0002

0.000150 0.00E+00

5.00E+03

0.0001

1.00E+04

1.50E+04

G.10

2.00E+04

2.50E+04

G.20

3.00E+04

3.50E+04

G.30

0.0010 0.00E+005.00E+021.00E+031.50E+032.00E+032.50E+033.00E+033.50E+034.00E+034.50E+035.00E+03 0.0008 G.10 G.20 G.30 0.0006 0.0004

0.00005

0.0002

0 0.00E+00 0.0009

0.0004 0.0008

2.00E+04 G.20

0.0001 FC.4 Building Height, G.10-G.30 σ2: 510.4338562 σ2: 575.8194234 σ2: 708.5323129 0.0018 μ: 4.19E+03 μ: 4.10E+03 μ: 4.15E+03 0 0.00E+00 1.00E+03 2.00E+03 3.00E+03 4.00E+03 5.00E+03 6.00E+03 7.00E+03 0.0016 0.0014

0.0003

0.0009 0.0005

1.50E+04

0.0006 0.0001

0 0.00E+00 0.01

0.0006

1.00E+04

G.10

0.0007 0.0002

0.005 0.015

0.0007

3.50E+04

G.30

0.0008 0.0003

0.01 0.02

0.0008

3.00E+04

0.0006

0.015 0.025

0 0.00E+00

5.00E+03

0.0007

FC.1 Block Density, G.10-G.30

0.02 0.03

2.50E+04

FC.3 Groundlevel Solar Exposure, G.10-G.30

0.00090

0.03

2.00E+04 G.20

FC.3 Groundlevel Solar Exposure, G.10-G.30 5.00E+03

1.00E+04

G.10

1.50E+04

2.00E+04

G.20

2.50E+04

3.00E+04

3.50E+04

G.30

G.10 G.20 G.30 Groundlevel Exposure,σG.10-G.30 2 2 σFC.3 : 1365.980841 σ2:Solar 1202.744628 : 1230.767149 μ: 27591.8976 μ: 26941.7088 μ: 26883.936

0 0.00E+005.00E+021.00E+031.50E+032.00E+032.50E+033.00E+033.50E+034.00E+034.50E+035.00E+03 G.10

G.10 σ2: 264.7852836 μ: 3.77E+03

G.20

G.20 σ2: 249.7002371 μ: 3.61E+03

G.30

G.30 σ2: 304.2907327 μ: 3.59E+03

0.0003 0.0007 0.0002 0.0006

Analysis 0.0001 & Observations 0.0005

It is worth 0.00040 mentioning that the algorithm correlates inverse0.00E+00 1.00E+03 2.00E+03 3.00E+03 4.00E+03 5.00E+03 6.00E+03 7.00E+03 ly proportionally the dimension of the local courtyards to the 0.0003 G.10 G.20 G.30 size of0.0002 the block in which it is located. This explains the absence 0.0001 of courtyards in some blocks, mainly in the experiment FC.4 Building Height, G.10-G.30 A, and0.0018 the 0 complete absence of buildings in some blocks of 0.00E+00 1.00E+03 3.00E+03 4.00E+03 5.00E+03 6.00E+03 7.00E+03 the experiment B. This2.00E+03 is an interesting observation, as it marks 0.0016 G.10 G.20 G.30 a great0.0014 difference in the way that the system is emerging in the two approaches, considering that the ranges of values and 0.0012 the parameters of both wereG.10-G.30 identical, in order FC.4 experiments Building Height, 0.001 0.0018 to enable 0.0008valid comparisons. We can safely conclude that even 0.0016 though the two experiments share the same parameters, genes 0.0006 0.0014 and ranges, experiment A allows for the formation of smaller 0.0004 0.0012 in size0.0002 blocks, with milder fluctuations, whereas the B experi0.001 ment present greater flexibility in the size of blocks’ configu0 0.0008 ration. 0.00E+005.00E+021.00E+031.50E+032.00E+032.50E+033.00E+033.50E+034.00E+034.50E+035.00E+03 0.0006

G.10

G.20

G.30

0.0004

The graphs of remapped values for each individual suggest 0.0002

57 |

Design Team 8 _1.00E+03 Mithuna Murugesh, Varvara Vasilatou 0.00E+00 5.00E+02 1.50E+03 2.00E+032.50E+03 3.00E+03 3.50E+034.00E+034.50E+035.00E+03 Emergence 2016 G.10

G.20

G.30

that our fitness criteria are working in pairs. Maximizing blocks’ density is not conflicting to minimizing ground level’s exposure, a relationship which was expected. But, minimizing building’s height is presented to work alongside minimizing the building’s footprint, a relationship rather unexpected. This observation is depicted in the patterns of population distribution according to each fitness criterion standard deviation graphs for each fitness criterion as well, where both FC.1 and FC.3 follow a general tendency of increasing variation. Only FC.2 reaches an early convergence, approaching a local peak. FC.4 presents an interesting differentiation from the other graphs, where in G.20 manages to translocate another peak, before moving downwards on G.30. Both the convergence in the individuals’ fitness value graph and the early convergence depicted in the FC.2, suggest to run the next part of the simulation with the aim of increasing the variation in order to locate other optimal solutions.

FC.2 Building Footprint, G.10-G.30 0.0003 0.00015 0.00025 0.0001

13. Sequence III| Generation XXX| Analysis & Observations 0.0002 0.00005

0.000150 0.00E+00

Experiment B 5.00E+03

0.0001

1.00E+04

1.50E+04

G.10

2.00E+04

2.50E+04

3.00E+04

G.20

3.50E+04

G.30

P o p u l a t i o n D i s t r i b u0.00005 t i o n G. 1 0 - G. 3 0 FC.1 Block Density, G.10-G.30

0 0.0006 0.00E+00

0.025

0.0005

0.02

FC.3 Groundlevel Solar Exposure, G.10-G.30 5.00E+03

1.00E+04

1.50E+04

G.10

2.00E+04

G.20

2.50E+04

3.00E+04

3.50E+04

G.30

0.0004

0.015

FC.1 Block Density, G.10-G.30

0.0003 0.0006

0.025 0.01

FC.3 Groundlevel Solar Exposure, G.10-G.30

0.0002 0.0005

0.02 0.005

0.0001 0.0004

0.0150 0.00E+00 5.00E+01 1.00E+02 1.50E+02 2.00E+02 2.50E+02 3.00E+02 3.50E+02 4.00E+02 4.50E+02

0.00030 0.00E+00 1.00E+03 2.00E+03 3.00E+03 4.00E+03 5.00E+03 6.00E+03 7.00E+03 8.00E+03

0.01

G.10

G.20

0.0002

G.30

G.10

G.20

G.30

0.0001

0.005

G.10 G.20 G.30 FC.2 Building Footprint, G.10-G.30 σ2: 18.5556712 σ2: 18.08502278 σ2: 19.58679641

0 0.0003 μ: 3.28E+02 3.31E+02 μ: 3.34E+02 4.00E+02 4.50E+02 0.00E+00 5.00E+01 1.00E+02 1.50E+02μ: 2.00E+02 2.50E+02 3.00E+02 3.50E+02 G.10

0.00025

G.20

G.10

0.0004

G.30

G.20

G.30

0 σ2: 691.5747896 σ2: 732.762499 σ2: 1073.04785 FC.4 Building Height, G.10-G.30 0.00E+00 1.00E+03 2.00E+03 3.00E+03 4.00E+03 5.00E+03 6.00E+03 7.00E+03 8.00E+03 μ: 3.78E+03 μ: 3.91E+03 μ: 4.16E+03 0.00045 G.10

G.20

G.30

0.00035

0.0002

0.0003

FC.2 Building Footprint, G.10-G.30

0.00015 0.0003

0.00025

FC.4 Building Height, G.10-G.30

0.00045 0.0002 0.0004 0.00015

0.0001 0.00025

0.00035 0.0001

0.00005 0.0002

0.0003 0.00005

0.000150 0.00E+00

5.00E+03

1.00E+04

1.50E+04

G.10

0.0001

2.00E+04

2.50E+04

3.00E+04

G.20

3.50E+04

G.30

0.000250 0.00E+002.00E+034.00E+036.00E+038.00E+031.00E+041.20E+041.40E+041.60E+041.80E+042.00E+04 0.0002 G.10 G.20 G.30 0.00015 0.0001

0.00005

0 0.0006 0.00E+00

0.0005 0.0004 0.0003 0.0006

FC.3 Groundlevel Solar Exposure, G.10-G.30 5.00E+03

1.00E+04

G.10

1.50E+04

2.00E+04 G.20

2.50E+04

3.00E+04

3.50E+04

G.30

G.10 G.20 G.30 Groundlevel Solar Exposure, σ2: FC.3 1520.523251 σ2: 1817.665568 σ2: G.10-G.30 2176.369532 μ: 26792.8704 μ: 25932.1536 μ: 25264.339

0 0.00E+002.00E+034.00E+036.00E+038.00E+031.00E+041.20E+041.40E+041.60E+041.80E+042.00E+04 G.10

G.10 σ2: 991.8472657 μ: 1.40E+04

G.20

G.20 σ2: 1150.122132 μ: 1.36E+04

G.30

G.30 σ2: 1268.675283 μ: 1.34E+04

0.0002 0.0005 0.0004 0.0001 0.00030 0.00E+00 1.00E+03 2.00E+03 3.00E+03 4.00E+03 5.00E+03 6.00E+03 7.00E+03 8.00E+03 0.0002

G.10

G.20

G.30

0.0001 0 0.00E+00 1.00E+03 2.00E+03 3.00E+03 4.00E+03 5.00E+03 6.00E+03 7.00E+03 8.00E+03

FC.4 Building Height, G.10-G.30

0.00045 0.0004

G.10

G.20

G.30

0.00035 0.0003 0.00025 0.00045 0.0002 0.0004

FC.4 Building Height, G.10-G.30

0.00015 0.00035 0.0001 0.0003 0.00005 0.00025 0.00020 0.00E+002.00E+034.00E+036.00E+038.00E+031.00E+041.20E+041.40E+041.60E+041.80E+042.00E+04 0.00015 G.10 G.20 G.30 0.0001 0.00005 0 0.00E+002.00E+034.00E+036.00E+038.00E+031.00E+041.20E+041.40E+041.60E+041.80E+042.00E+04 G.10

G.20

G.30

Analysis & Observations

The convergence graph of generation 30 shows divergence in all criteria signs of maintaining a similer convergence from the first generation to generation 30, except FCI. The fittest in FC1 remains the same, The fittest in FCII is the increases, the fittest in FC III reduces slightly and the FC IV reducesslightly. The individuals are ranked according to the value of ﬁtness. The variation in ﬁtness value and two groups of similarity in morphology indicate that the simulation was attempting to evolve and develop the same individual but the low rate of mutation restrained the evolution thus resulting in this standard deviation graph. Emergent Technologies & Design | 58 AA School of Architecture

14. Sequence III| Generation LX Experiment A

Block Density

Building Footprint

Ground exposure

Building Height

G.60.1

G.60.15

G.60.22

G.60.12

FITTEST

297 m3 26340.4 m2 3418 m2 3930 m

G.60.12

LEAST FIT

296.7m3 27956.1 m2 2677 m2 4159 m

241.6 m3 23843.5 m2 5495 m2 3276 m

G.60.35

201 m3 24186 m2 4701 m2 2854 m

G.60.46

288.4 m3 28396.8 m2 2867 m2 4108 m

228.1 m3 24235 m2 5753 m2 2961 m

201 m3 24186 m2 4701 m2 2854 m

G.60.22

296.7m3 27956.1 m2 2677 m2 4159 m

General

The breeding strategy is revised prior to the simulation of the next 30 generations, based on the conducted analysis of G.30, and tested to enhance variation. Therefore, the mutation probability and rate are increased to 0.2 and 0.7 respectively, while the values of elitism and crossover rate are unaltered. Three individuals are selected and marked as preferred in Octopus, that were in an average position, satisfying to an extent all the fitness criteria, in order to direct the simulation to produce more average results and increase the variation.

max Block Density min Building Footprint min Ground level Exposure min Buildingsâ€™ Height

Simulation

se t - u p

Elitism Mutation probality Mutation rate Crossover rate

0.1

0.2

0.5

* 3 phenotypes marked objectives

59Â |

Design Team 8 _ Mithuna Murugesh, Varvara Vasilatou Emergence 2016

1.0

Observations

The graphs suggest a slight increase in the variation, which is depicted in the increased morphological variation of the phenotypes as compared to the previous simulation. The secondary network of connections between the courtyards present two main configurations in the directionality of the streets. The ones in the x direction are mainly presented parallel to each other, whereas the ones in the y direction are mostly oblique, deviating from the vertical.

14. Sequence III| Generation LX Experiment B

Block Density

FITTEST

Building Footprint

Ground exposure

G.60.49

G.60.44

G.60.37

383.1 m3 23647.6 m2 4794 m2 12918 m

294.8 m3 16254.7 m2 10172 m2 8904 m

G.60.32

LEAST FIT

284.7 m3 16352.6 m2 10294 m2 8385

G.60.42

372.3 m3 28984.3 m2 3032 m2 15864 m

Elitism Mutation probality Mutation rate Crossover rate

0.2

342 m3 17576.6 m2 10924 m2 9450 m

284.7 m3 16352.6 m2 10294 m2 8385

G.60.42

372.3 m3 28984.3 m2 3032 m2 15864 m

The breeding strategy is revised prior to this simulation.The simulation is run from generation 30 to generation 60, which produced 50 individuals in the last generation. The mutation probability is increased to 2.5 while the value of mutation rate is retained to its previous simulation.

se t - u p 0.1

G.60.35

G.60.32

General

max Block Density min Building Footprint min Ground level Exposure min Buildingsâ€™ Height

Simulation

313.2 m3 27466.5 m2 2534 m2 13896 m

Building Height

Observations 0.5

1.0

It is observed that there is a slight increase in the morphological variation of the phenotypes as com pared to the previous simulation that produced two groups of similar individuals. However, a pattern can be seen in this generation where the network seem to align to an orthogonal pattern in particular the vertical connectors. Some of the main streets seem to disapear. Emergent Technologies & Design Â | 60 AA School of Architecture

0.0003 0.00025 0.0002

14. Sequence III| Generation LX| Analysis & Observations 0.0001 0.00015

Experiment A

0.00005 0 0.00E+00 5.00E+03 1.00E+04 1.50E+04 2.00E+04 2.50E+04 3.00E+04 3.50E+04 G.10

G.20

G.30

P o p u l a t i o n D i s t r i b u t i o n G. 1 0 - G. 3 0 FC.1 Block Density, G.10-G.60

G.50

G.60

FC.3: Groundlevel Solar Exposure, G.10-G.60

0.03

0.0009 0.0008

0.025

0.0007

FC.1 Block Density, G.10-G.60

0.02

0.0006

0.03 0.015

0.0005 0.0004

0.025 0.01

0.0003

0.02 0.005

0.0002

0.015 0 0.00E+00 0.01

0.0001

5.00E+01 G.10

0.005

1.00E+02 G.20

1.50E+02 G.30

2.00E+02 G.40

2.50E+02 G.50

3.00E+02

0.00045

G.10

G.20

G.30

G.40

G.50

0 0.00E+00 1.00E+03 2.00E+03 3.00E+03 4.00E+03 5.00E+03 6.00E+03 7.00E+03

3.50E+02

G.60

G.30 G.40 G.50 G.60 0 2 2 2 σ0.00E+00 : 24.97241203 σ1.00E+02 : 24.68615082 σ2.00E+02 : 24.67717931 σ23.00E+02 : 26.32840235 5.00E+01FC.2 1.50E+02Footprint, 2.50E+02 3.50E+02 Building G.10-G.60 μ: 2.51E+02 μ: 2.45E+02 μ: 2.41E+02 μ: 2.54E+02 G.60

G.10

G.20

G.30

G.40

G.50

G.60

G.30 G.40 G.50 G.60 2 σ2: 708.5323129 σ2: 658.8587712 : 644.1249245G.10-G.60 σ2: 811.892585 FC.3: Groundlevel Solarσ Exposure, μ: 4.15E+03 μ: 4.10E+03 μ: 4.22E+03 μ: 3.99E+03

0.0018 0.0016

0.0004

0.0014

0.00035

FC.2 Building Footprint, G.10-G.60

0.0003

FC.4: Building Height, G.10-G.60

0.0012

0.00025 0.00045

0.001 0.0018

0.0002 0.0004

0.0008 0.0016

0.00015 0.00035

0.0006 0.0014

0.0001 0.0003

0.0004 0.0012

0.00005 0.00025

0.0002 0.001

0 0.0002 0.00E+00 5.00E+03 1.00E+04 1.50E+04 2.00E+04 2.50E+04 3.00E+04 3.50E+04 0.00015 G.10 G.20 G.30 G.40 G.50 G.60 0.0001

0 0.0008 0.00E+00 5.00E+02 1.00E+03 1.50E+03 2.00E+03 2.50E+03 3.00E+03 3.50E+03 4.00E+03 4.50E+03 5.00E+03 0.0006 G.10 G.20 G.30 G.40 G.50 G.60 0.0004

0.00005

0.0002

0 0.00E+00 5.00E+03 1.00E+04 1.50E+04 2.00E+04 2.50E+04 3.00E+04 3.50E+04

FC.3: Groundlevel Solar Exposure, G.10-G.60

0.0009

G.10

G.20

G.30

G.40

G.50

G.60

0.0008

G.30 G.40 G.50 G.60 24.97241203 σ2: 1130.457032 σ2: 1000.412752 σ2: 1289.842814 FC.3: Groundlevel Solar μ:Exposure, μ: 26480.5056 25946.8416 G.10-G.60 μ: 26080.992 0.0006μ: 2.51E+02 0.0007 σ2:

0.0009 0.0005 0.0008 0.0004 0.0007 0.0003 0.0006 0.0002 0.0005 0.0001 0.0004 0 0.00E+00 1.00E+03 2.00E+03 3.00E+03 4.00E+03 5.00E+03 6.00E+03 7.00E+03 0.0003

Analysis & Observations

The standard deviation graphs of the population distributions depict a general tendency towards variation,G.40as expected. signs G.10 G.20 G.30 G.50 G.60 of di0.0002 vergence for all the fitness criteria as expected. The FC.1 and FC.3 0.0001 graphs present a gradual decrease from G.10 to G.60, whereas the 0 FC.3: Groundlevel Solar 4.00E+03 Exposure, G.10-G.60 0.00E+00 1.00E+03 2.00E+03 throughout 3.00E+03 6.00E+03 7.00E+03 a FC.4 depict wide fluctuations the5.00E+03 simulation following 0.0018trend.G.10 G.20 G.30 G.40 G.50 G.60 downward 0.0016

The FC0.0014 of ground level exposure and building height would expect FC.3: Groundlevel Solar Exposure, G.10-G.60 global 0.0012 more to produce uniform configuration of the superblock’s 0.0018 0.001 geometry. However, the simulation’s results do not present a clear 0.0016 pattern0.0008 of the heights, due to the number of buildings and the set0.0014 0.0006 up of the algorithm, that affects each buildings’ height individually. 0.0012 0.0004 Therefore, for the next step, we could pursue a more abstract idea 0.001 0.0002 of the urban blocks, by merging some buildings together, or altering 0.0008 0 the definition in such way that we could create relationships on the 0.00E+005.00E+021.00E+031.50E+032.00E+032.50E+033.00E+033.50E+034.00E+034.50E+035.00E+03 0.0006 height of the neighbouring buildings though an emergent system like 0.0004

61 |

G.40

G.10

G.20

G.30

G.40

G.50

G.60

0.0002

Design Team 8 _ Mithuna Murugesh, Varvara Vasilatou 0 Emergence 2016

0.00E+005.00E+021.00E+031.50E+032.00E+032.50E+033.00E+033.50E+034.00E+034.50E+035.00E+03 G.10

G.20

G.30

G.40

G.50

G.60

0 0.00E+00 5.00E+02 1.00E+03 1.50E+03 2.00E+03 2.50E+03 3.00E+03 3.50E+03 4.00E+03 4.50E+03 5.00E+03 G.10

G.20

G.30

G.40

G.50

G.60

G.30 G.40 G.50 G.60 σ2: 708.5323129 σ2: 324.2579196 σ2: 312.1901222 σ2: 357.5887028 μ: 3.47E+03 μ: 4.10E+03 μ: 3.40E+03 μ: 3.54E+03

the cellular automata. In this way, octopus could have the possibility to combine and entwine different patterns that would have a more holistic impact on the superblock canvas. It is worth mentioning that the mean values of each fitness criterion are ameliorating towards the objective, even if the trend is slight for each fitness criterion, as shown in the graphs. The morphological configuration of the phenotypes indicates slight variations without dominant features that could be analysed or further pattern that could be observed. This suggests that the definition’s gene pool should be revised in terms of counting and range. In contradiction, in experiment B there is a marked discrepancy between the two courtyards’ networks configuration, with the local courtyards emerging in many individuals to exceed the main courtyards size and shape.

0.00015

FC.2 Building Footprint, G.10-G.60

0.0003 0.0001 0.00025 0.00005

14. Sequence III| Generation LX| Analysis & Observations 0.0002

0 0.00E+00 0.00015 0.0001

5.00E+03

G.10

E x1.50E+04 p e r i2.00E+04 men t B3.00E+04 2.50E+04

1.00E+04

G.20

P o p u l a t i o n D i s t r i b u0.00005 t i o n G. 1 0 - G. 3 0 0 0.00E+00

FC.1 Block Density, G.10-G.60

0.025

5.00E+03

1.00E+04

G.30

1.50E+04

G.40

2.00E+04

G.50

2.50E+04

3.50E+04

G.60

3.00E+04

3.50E+04

FC.3: Groundlevel Solar Exposure, G.10-G.60

0.0006 G.10

G.20

G.30

G.40

G.50

G.60

0.0005

0.02

0.0004

FC.1 Block Density, G.10-G.60

0.015 0.025

FC.3: Groundlevel Solar Exposure, G.10-G.60

0.0003 0.0006 0.01 0.02

0.0002 0.0005

0.005 0.015

0.0001 0.0004

0 0.01 0.00E+00 5.00E+01 1.00E+02 1.50E+02 2.00E+02 2.50E+02 3.00E+02 3.50E+02 4.00E+02 4.50E+02

0 0.0003 -2.00E+03 0.00E+00

2.00E+03 4.00E+03 6.00E+03 8.00E+03 1.00E+04 1.20E+04 1.40E+04

0.0002

0.005

G.10

G.20

G.30

G.40

G.50

G.60

0.0001

0 0.00E+00 5.00E+01 1.00E+02 1.50E+02 2.00E+02 2.50E+02 3.00E+02 3.50E+02 4.00E+02 4.50E+02

G.30 G.40 G.50 G.60 σ2: 19.58679641FC.2 σ2: 16.8798708 σ2: 19.5807607 σ2: 22.18842427 Building Footprint, G.10-G.60 G.10 G.20 G.30 G.60 0.0003μ: 3.34E+02 μ: 3.32E+02 μ:G.40 3.32E+02G.50 μ: 3.33E+02 0.00025

G.50

G.60

G.40 G.50 G.60 2.00E+03 4.00E+03 6.00E+03 8.00E+03 1.00E+04 1.20E+04 1.40E+04 FC.4: min Height, G.10-G.60 σ2: 2224.022464 σ2: 1073.04785 σ2: Building 1125.223875 σ2: 1617.391518 μ: 4.16E+03 μ: 4.24E+03 μ: 4.83E+03 μ: 5.93E+03

0.0004

G.10

G.20

G.30

G.40

G.50

G.60

FC.4: min Building Height, G.10-G.60

0.0002 0.0004 0.00015 0.00035 0.0001 0.0003

0.0002 0.00005

0.0006

G.40

0.00045

0.00025 0.00045

0.00025 0.0001

0 0.00E+00

G.30

0 G.30 -2.00E+03 0.00E+00

0.0003

FC.2 Building Footprint, G.10-G.60

0.0003 0.00015

0.00005

G.20

0.00035

0.0002

0.00015 0 0.00E+00 0.0001

G.10

0.00005 0.00025 5.00E+03

G.10

1.00E+04

G.20

1.50E+04

G.30

2.00E+04

G.40

2.50E+04

G.50

3.00E+04

3.50E+04

0 0.0002 0.00E+002.00E+034.00E+036.00E+038.00E+031.00E+041.20E+041.40E+041.60E+041.80E+042.00E+04 0.00015 0.0001

G.60

0.00005

5.00E+03

G.10

1.00E+04

1.50E+04

2.00E+04

2.50E+04

3.00E+04

3.50E+04

FC.3: Groundlevel Solar Exposure, G.10-G.60 G.20

G.30

G.40

G.50

G.60

0.0005

G.30 G.40 G.50 G.60 2176.369532 σ2: 2482.733745 σ2: 3151.822579 σ2: 3253.851396 Groundlevel Solar Exposure, μ: 25264.339 FC.3: μ: 24844.2624 μ: 23794.56 μ: G.10-G.60 21957.5808 0.0006 σ: 0.0004 2

G.10

G.20

G.30

G.40

G.50

G.60

0 0.00E+002.00E+034.00E+036.00E+038.00E+031.00E+041.20E+041.40E+041.60E+041.80E+042.00E+04

G.10

G.20

G.30

G.40

G.50

G.60

G.40 G.50 G.60 G.30 σ2: 1268.675283 σ2: 1360.570616 σ2: 1716.05801 σ2: 1693.89794 μ: 1.32E+04 μ: 1.27E+04 μ: 1.17E+04 μ: 1.34E+04

0.0003

0.0005 0.0002 0.0004 0.0001 0.0003 0 -2.00E+03 0.00E+00 2.00E+03 4.00E+03 6.00E+03 8.00E+03 1.00E+04 1.20E+04 1.40E+04 0.0002 0.0001

G.10

G.20

G.30

G.40

G.50

G.60

0 -2.00E+03 0.00E+00 2.00E+03 4.00E+03 6.00E+03 8.00E+03 1.00E+04 1.20E+04 1.40E+04 0.00045

FC.4: min Building Height, G.10-G.60 G.10

G.20

G.30

G.40

G.50

G.60

0.0004 0.00035 0.0003 0.00025 0.00045

FC.4: min Building Height, G.10-G.60

0.0002 0.0004 0.00015 0.00035 0.0001 0.0003 0.00005 0.00025

0.00020 0.00E+002.00E+034.00E+036.00E+038.00E+031.00E+041.20E+041.40E+041.60E+041.80E+042.00E+04 0.00015 0.0001

G.10

G.20

G.30

G.40

G.50

G.60

0.00005 0 0.00E+002.00E+034.00E+036.00E+038.00E+031.00E+041.20E+041.40E+041.60E+041.80E+042.00E+04

G.10

G.20

G.30

G.40

G.50

G.60

Analysis & Observations

The standard deviation graph of generation 30 to 60 shows signs of divergence from the 3oth generation to generation 60. The overall fitness across all four criteria incurs a change. The fittest in FC1 decreases minimally, The fittest in FCII decreases slightly, the fittest in FC III reduces slightly and the FC IV also slightly. The individuals are ranked according to the value of ﬁtness. The variation in ﬁtness value but similarity in morphology indicate that the simulation was attempting to evolve and develop the same individual but the low rate of mutation restrained the evolution thus resulting in redundancy and stability of in the standard deviation graph. Emergent Technologies & Design | 62 AA School of Architecture

15. Sequence III| Generation C Experiment A

Block Density

Building Footprint

Ground exposure

Building Height

G.100.41

G.100.24

G.100.4

G.100.23

FITTEST

306 m3 27163 m2 3040 m2 4045 m2

G.100.23

LEAST FIT

278.5 m3 26928 m2 2689 m2 3954 m

212.2 m3 22815.3 m2 5760 m2 2847 m

G.100.5

194.7 m3 23256 m2 5063 m2 2800 m

G.100.24

295.7 m3 27858.2 m2 3081 m2 3900 m

212.2 m3 22815.3 m2 5760 m2 2847 m

194.7 m3 23256 m2 5063 m2 2800 m

G.100.13

294.3 m3 27711.36 m2 2732 m2 4139 m

General

The breeding strategy is revised based on previous results and tested in this simulation to achieve convergence and optimum ﬁtness. Thus, the elitism is increased to 0.7, mutation probability and mutation rate are reduced to 0.1 and 0.5 respectively. and the marked individuals are unmarked.

max Block Density min Building Footprint min Ground level Exposure min Buildings’ Height

Observations

Simulation Set-up Elitism Mutation probality Mutation rate Crossover rate

63 |

0.1

0.2

0.5

Design Team 8 _ Mithuna Murugesh, Varvara Vasilatou Emergence 2016

1.0

The fittest individuals for the FC of building footprint is the least fit for the ground level exposure and the fittest individual for the building height is the least fit one for the block density. As well, the fittest individuals for block density and ground level exposure are almost identical. The same observation is maid for the individuals of building footprint and building height. All these observations strengthen our hypothesis that our fitness criteria work in pairs and are conflicting in pairs.

15. Sequence III| Generation C Experiment B

Block Density

FITTEST

G.100.7

330 m3 24382 m2 3569 m2 13008 m

LEAST FIT

G.100.10

346.9 m3 15960.9 m2 11148 m2 8511 m

Building Footprint

G.100.43

G.100.1

0.2

0.5

305 m3 20856.9 m2 4884 m2 10677 m

G.100.43

312.2 m3 20710 m2 6401 m2 10941

307 m3 15275.5 m2 11348 m2 7785 m

Building Height

G.100.43

346.9 m3 15960.9 m2 11148 m2 8511 m

G.100.1

312.2 m3 20710 m2 6407 m2 10941 m

General

The breeding strategy is revised based on previous results and tested in this simulation to achieve convergence and optimum ﬁtness. Thus, the elitism is increased to 0.8, mutation probabilty and mutation rate is reduced to 0.1 and 0.5 respectively. No interim generations are evaluated or analyzed in this simulation.

Simulation Set-up 0.1

G.100.20

307 m3 15275.5 m2 11348 m2 7785 m

max Block Density min Building Footprint min Ground level Exposure min Buildings’ Height

Elitism Mutation probality Mutation rate Crossover rate

Ground exposure

1.0

Observations

It is observed that nearly all the phenotypes produced in generation 100 are exactly identical with only the gene pool of position of main courtyard creating a variation in the individuals. It appears that two groups of similar phenotypes are created with the only difference being the position of the main courtyards. Emergent Technologies & Design | 64 AA School of Architecture

FC.2 Building Footprint, G.30.-G.100

0.0003

0.00035

0.00025

0.0003

0.0002

15. Sequence III| Generation C| Analysis & Observations 0.00025 0.00015

Experiment A

0.0002

0.0001

0.00015

0.00005

0.0001 0 0.00E+00

0.00005

5.00E+03

1.00E+04

G.30

1.50E+04

O v e r a l P o p u l a t i o n 0D i s t r i b u t i o n

2.00E+04

G.60

2.50E+04

3.00E+04

3.50E+04

G.100

0.00E+00 5.00E+03 1.00E+04 1.50E+04 2.00E+04 2.50E+04 3.00E+04 3.50E+04 G.30

FC.1 Block Density, G.30.-G.100

0.018

G.60

G.100

FC.3 Groundlevel Exposure, G.30.-G.100 0.0006

0.016

0.0005

0.014

FC.1 Block Density, G.30.-G.100

0.012 0.018 0.01 0.016 0.008 0.014 0.006 0.012 0.004 0.01 0.002 0.008 0 0.006 0.00E+00 5.00E+01 1.00E+02 1.50E+02 2.00E+02 2.50E+02 3.00E+02 3.50E+02 4.00E+02

0.0006 0.0004

0.004

0.0001

G.30

0.002

G.30

G.60

G.100

0 σ2: 15.87220977 σ2: 19.20711269 σ2: 30.0392003 5.00E+01 1.00E+02 1.50E+02 0.00E+00 2.00E+02 2.50E+02 3.00E+02 3.50E+02 4.00E+02 0.00035

FC.2 Building Footprint, G.30.-G.100 μ: 2.58E+02 μ: 2.49E+02 μ: 2.48E+02 G.30

G.60

G.100

0.0005 0.0003 0.0002 0.0004 0.0001 0.0003 0 0.00E+00 1.00E+03 2.00E+03 3.00E+03 4.00E+03 5.00E+03 6.00E+03 7.00E+03 8.00E+03

0.0002

FC.2 Building Footprint, G.30.-G.100

0.001

0.0003 0.00015

0.0006 0.0012

0.00025 0.0001

0.0004 0.001

0.0002 0.00005

0.0002 0.0008

0.00015 0 0.00E+00 5.00E+03 1.00E+04 1.50E+04 2.00E+04 2.50E+04 3.00E+04 3.50E+04 0.0001

0.0006

G.30

G.60

G.100

0 0.00E+00 5.00E+03 1.00E+04 1.50E+04 2.00E+04 2.50E+04 3.00E+04 3.50E+04

0.0005 0.0004 0.0006

G.100

FC.4 Building Height, G.30.-G.100

0.0014

0.0014 0.0008

0.0006

G.60

G.30 G.60 G.100 0 0.00E+00 2.00E+03 3.00E+03 4.00E+03 5.00E+03 6.00E+03 7.00E+03 8.00E+03 σ2: 1.00E+03 510.4338562 σ2: 575.8194234 σ2: 901.8321194

0.00035 0.0002

0.00005

G.30

μ: 4.19E+03 G.30

μ: 4.10E+03 G.60

μ:G.100 4.21E+03

0.0012

0.0003 0.00025

FC.3 Groundlevel Exposure, G.30.-G.100

FC.3G.30 Groundlevel Exposure, G.30.-G.100 G.60 G.100

G.30 G.60 G.100 σ2: 1365.980841 σ2: 1202.744628 σ2: 1351.16295 μ: 27591.8976 μ: 26941.7088 μ: 25385.76 FC.3 Groundlevel Exposure, G.30.-G.100

FC.4 Building Height, G.30.-G.100

0 0.00E+005.00E+021.00E+031.50E+032.00E+032.50E+033.00E+033.50E+034.00E+034.50E+035.00E+03

0.0004

G.30

G.60

G.100

0.0002 0 0.00E+005.00E+021.00E+031.50E+032.00E+032.50E+033.00E+033.50E+034.00E+034.50E+035.00E+03 G.30

G.30 σ2: 264.7852836 μ: 3.77E+03

G.60

G.60 σ2: 249.7002371 μ: 3.61E+03

G.100

G.100 σ2: 395.3041341 μ: 3.39E+03

0.0003 0.0005 0.0002 0.0004 0.0001 0.0003 0 0.0002 0.00E+00 1.00E+03 2.00E+03 3.00E+03 4.00E+03 5.00E+03 6.00E+03 7.00E+03 8.00E+03 0.0001

G.30

G.60

G.100

0 Analysis0.00E+00 & Observations 1.00E+03 2.00E+03 3.00E+03 4.00E+03 5.00E+03 6.00E+03 7.00E+03 8.00E+03

Buildingfor Height, G.30.-G.100 The breeding strategyFC.4 undertaken G.30 G.60this generation G.100was aim0.0014 ing into achieving a convergence to all fitness criteria. However, the 0.0012 graphs suggest a mild divergence towards all the fitness criteria.0.001 The analysis FC.4 conducted to all the generations Building Height, G.30.-G.100until the last one0.0014 showed that there was presented convergence in earlier 0.0008 generations 0.0012 before concluding again to divergence. This phe0.0006 nomenon can be explained by the fact that either our fitness 0.001 criteria0.0004 are very conflicting to allow the Octopus define an op0.0008 timal solution, or our breeding strategies were not aggressive 0.0002 enough0.0006 and the simulation needed more computational time 0

0.00E+005.00E+021.00E+031.50E+032.00E+032.50E+033.00E+033.50E+034.00E+034.50E+035.00E+03 0.0004 0.0002

65 |

G.30

G.60

G.100

Design 0 Team 8 _ Mithuna Murugesh, Varvara Vasilatou 0.00E+00 5.00E+021.00E+031.50E+032.00E+032.50E+033.00E+033.50E+034.00E+034.50E+035.00E+03 Emergence 2016 G.30

G.60

G.100

in terms of generations to reach our goal or the gh definition needed further configuration for indicating further relations or genes and altering possible errors or structures. In any case, further simulations should be set up and run in order to identify the issue with certainty and thus define the best solution. Moreover, through the selection of only the fittest and the least fit of each selected generation for analysis, we were analysing the extremes of the generation for each fitness criteria, without considering individuals that maybe have greater morphological variations, satisfying two different fitness criteria.

0.00018 0.00016 0.00014 0.00012 0.0002 0.0001 0.00018 0.00008 0.00016 0.00006 0.00014 0.00004 0.00012 0.00002 0.0001 0 0.00008 0.00E+00 0.00006

FC.2 Building Footprint, G.30-G.100

15. Sequence III| Generation C| Analysis & Observations Experiment B

5.00E+03

1.00E+04

1.50E+04

G.30

2.00E+04

2.50E+04

G.60

O v e r a l P o p u l a t i o0.00004 n Distribution

3.00E+04

3.50E+04

G.100

0.00002

0 0.00E+00

FC.1 Block Density, G.30-G.100 0.025

0.0004

FC.1 Block Density, G.30-G.100

0.0003

0.025 0.015

0.00025 0.0004 0.0002 0.00035 0.00015 0.0003 0.0001

0.02 0.01 0.015 0.005

G.60

3.50E+04

G.100

FC.3 Groundlevel Exposure, G.30-G.100

0.00025 0.00005

0.010 0.00E+005.00E+011.00E+021.50E+022.00E+022.50E+023.00E+023.50E+024.00E+024.50E+02 G.60 G.100 G.30 0.005

G.30 G.60 G.100 σ2: 22.18842427 σ2: 27.03374279 0 σ : 19.58679641 FC.2 Building Footprint, G.30-G.100 0.00E+005.00E+011.00E+021.50E+022.00E+022.50E+023.00E+023.50E+024.00E+024.50E+02 μ: 3.34E+02 μ: 3.33E+02 μ: 3.32E+02 2

0.0002

G.30

G.60

G.100

0.00018

0.0002 0 -2.00E+03 0.00E+002.00E+034.00E+036.00E+038.00E+031.00E+041.20E+041.40E+041.60E+041.80E+04 0.00015 G.30

0.0001 0.00005

G.60

G.100

G.30

G.60

G.100

μ: 4.16E+03

μ: 5.93E+03

μ: 7.69E+03

0 σ2: 1073.04785 σ2: 2224.022464 σ2: 2827.164771 FC.4 Building Height, G.30-G.100 -2.00E+03 0.00E+00 2.00E+03 4.00E+036.00E+03 8.00E+03 1.00E+041.20E+041.40E+041.60E+041.80E+04 0.00035

G.30

G.60

G.100

0.0003

0.00016 0.00014

FC.2 Building Footprint, G.30-G.100

0.00012 0.0002 0.0001

0.00018 0.00008 0.00016 0.00006 0.00014 0.00004 0.00012 0.00002 0.00010 0.000080.00E+00 5.00E+03 1.00E+04 1.50E+04 2.00E+04 2.50E+04 3.00E+04 3.50E+04 G.30 G.60 G.100 0.00006 0.00004

0.00025 0.0002 0.00035

FC.4 Building Height, G.30-G.100

0.00015 0.0003 0.0001 0.00025 0.00005 0.0002 0 0.00015 0.00E+002.00E+034.00E+036.00E+038.00E+031.00E+041.20E+041.40E+041.60E+041.80E+042.00E+04

0.0001

G.30

G.60

G.100

0.00005

0.00002 0 FC.3 Groundlevel Exposure, G.30-G.100 0.00E+00 5.00E+03 1.00E+04 1.50E+04 2.00E+04 2.50E+04 3.00E+04 3.50E+04 0.0004 G.30 G.60 G.100 0.00035

G.30 G.60 G.100 2176.369532 σ2: 3253.851396 σ2: 3386.577746 FC.3 Groundlevel Exposure, G.30-G.100 μ: 25264.339 μ: 21957.5808 μ: 18981.792

0.00025 σ2:

0.0002 0.0004 0.00015 0.00035 0.0001 0.0003 0.00005 0.00025 0 0.0002 -2.00E+03 0.00E+002.00E+034.00E+036.00E+038.00E+031.00E+041.20E+041.40E+041.60E+041.80E+04

0.00015

G.30

0.00035

0.02

0.0003

5.00E+03 1.00E+04 1.50E+04 2.00E+04 G.30-G.100 2.50E+04 3.00E+04 FC.3 Groundlevel Exposure,

G.30

G.60

0 0.00E+002.00E+034.00E+036.00E+038.00E+031.00E+041.20E+041.40E+041.60E+041.80E+042.00E+04 G.30

G.30 σ2: 1268.675283 μ: 1.34E+04

G.60

G.60 σ2: 1693.89794 μ: 1.17E+04

G.100

G.100 σ2: 1889.48715 μ: 1.02E+04

G.100

0.0001 0.00005

FC.4 Building Height, G.30-G.100

0 0.00035 -2.00E+03 0.00E+002.00E+034.00E+036.00E+038.00E+031.00E+041.20E+041.40E+041.60E+041.80E+04 0.0003

G.30

G.60

G.100

0.00025 0.0002 0.00015 0.00035

Analysis & Observations

FC.4 Building Height, G.30-G.100

0.0001 0.0003 0.00005 0.00025 0 0.00020.00E+002.00E+034.00E+036.00E+038.00E+031.00E+041.20E+041.40E+041.60E+041.80E+042.00E+04

0.00015

G.30

G.60

G.100

0.0001

The fittest in FC1 deacreses, The fittest in FCII incraeses, the fittest in FC III reduces slightly and the FC IV reduces slightly. According to our breeding strategy the graph should show a convergence however it shows an increase in variation for all four fitness criteria. We were unable to find a reason for this phenomena at this generation.

0.00005 0 0.00E+002.00E+034.00E+036.00E+038.00E+031.00E+041.20E+041.40E+041.60E+041.80E+042.00E+04 G.30

G.60

G.100

Emergent Technologies & Design | 66 AA School of Architecture

Sequence III| Analysis & Observations Experiment A

G.30

Elitism: Mutation probability: Mutation rate: Crossover rate:

G.60

0.2 0.1 0.5 0.5

Elitism: Mutation probability: Mutation rate: Crossover rate:

G.100

0.2 0.2 0.7 0.5

Elitism: Mutation probability: Mutation rate: Crossover rate:

0.7 0.1 0.5 0.5

* 3 phenotypes marked objectives Convergence Graph

Convergence Graph

The graph depicts the comparison of the three evaluated generations in this sequence for experiment A

Analysis & Observations

The 1st Delaunay graph (G.30) indicates that the is a general tendency of a linearity among the results, moving from top left to bottom right. Nevertheless, the genetic distance graph indicates a great variation in the genome distribution. This observation implies the fact that our fitness criteria are working in pairs. In order to tackle this phenomenon, we try to increase variation and divergence by introducing in the second set of experiments (G.60) drastic mutation and marking 3 phenotypes situated in strategic positions for our goal. In the 2nd graph the highlighted spheres indicate the position of the

67Â |

Design Team 8 _ Mithuna Murugesh, Varvara Vasilatou Emergence 2016

selected individuals along the overall population. This graph presents greater variation that the previous one and the curvature of the Delaunay graph towards the 0, 0 is augmented. On this stage, we are revising our breeding strategy and altering it towards achieving convergence for defining optimal solutions. Therefore, we unmarked the preferred individuals, decrease the mutation and increase drastically the elitism rate to 0.7. On this case, the genetic distance graph indicates a convergence in the genome configurations, though the Delaunay graph cannot drive into safe conclusions.

Sequence III| Analysis & Observations Experiment B

G.30

Elitism: Mutation probability: Mutation rate: Crossover rate:

Convergence Graph

G.60

0.2 0.1 0.5 0.5

Elitism: Mutation probability: Mutation rate: Crossover rate:

Convergence Graph

G.100

0.2 0.25 0.5 0.5

Elitism: Mutation probability: Mutation rate: Crossover rate:

0.5 0.1 0.5 0.5

Convergence Graph

The graph shows the comparison of the three evaluated generations in this sequence for experiment B

Emergent Technologies & Design Â | 68 AA School of Architecture

69Â |

Design Team 8 _ Mithuna Murugesh, Varvara Vasilatou Emergence 2016

Conclusion Octopus is indeed a powerful tool. However, in order to fully activate its pontential, the most important part is retained inside the environment of grasshoper and refers to the way the user is building his definition, the system, defines the interrelations, the genes and the parameters. This point encompasses the level of difficulty of such an experimentation, which requires skills and experience that excceed the frameworks of a 3 weeks workshop. Therefore, for us, this workshop was just the starting point to understand the complexity of such an endeavor. Comparing the results of our experiments in the last sequence, where we analysed a superblock as part of an actual urban environment, we can conclude that due to one main difference of the definition, retaining the end points of the main streets fixed or enabling flexibility, within each experiment signiﬁcant phenotypical diversity was emerged. In the urban context, these solvers can provide us results with many surprises. It would be rather meaningless if the designer could actually predict all the possible outcomes or the ﬁttest individuals of such an evolutionary design process. Although an evolved form is realised and observed in individual entities, the evolutionary method of thinking implies that the population and not the individual is the matrix for the production and alteration.

Through the work developed in this research, concepts from Evolution and Embryological Development (Evo-Devo) theories are investigated and applied as a design approach through Evolutionary Computational processes. The developed experiments are based on three main consecutive sequences, which start from applying standard operations to a simple abstract geometry and progressively become more complex once the application is considered for an urban design context. In the first sequences it was possible to create populations of individuals in an “analogical” way, by 3D modelling in Rhino environment simple geometrical operations that where applied to a basic primitive. These empirical simplified experiments helped gain a basic understanding of evolutionary algorithms, and how to think about the design of strategies for evolutionary processes. In both 2nd and 3rd sequences, four criteria were defined. However, although somewhat harder than for the first experiments, it was still possible to direct the procedure and manipulate the outcome through the continuous process of data analysis, scriting redefinition and breeding strategies’ revision. Furthermore, with the introduction of mutation operations it was possible to enhace variation in order to tackle early convergence to a local peak. The final sequences aimed for an urban design application and therefore encompassed more complex design experiments, involving a larger number of parameters and criteria. The potential of Octopus/Gh plugin within Rhino as a tool for finding multiple optimisation solutions was studied through the simulation of a genetic experiment for the evolution of urban blocks. Much of the control that had been gained in previous simplified experiment was lost once this new digital environment was introduced. However it was still possible to establish a strategy, by adjusting some variable values such as elitism, crossover rate, mutation probability and mutation rate. The utilisation of Octopus introduces a degree of complexity which enables to run a large number of generations in a short period of time, and to evaluate the optimal solution for up to 5 Fitness criteria, producing a range of optimised trade-off solutions between the extremes of each goal. This was proven to be an extremely useful tool for the application of evolutionary principles to design, and for the finding of optimal solutions, that would otherwise be impossible to anticipate.

Emergent Technologies & Design | 70 AA School of Architecture

Architectural Association School of Architecture - Emergent Techno

EMERGENCE AND DESIGN SEMINAR Shibam - 16th century

Primitie Block:

Block:

1-G1.1

Block Length: Block Width: Street Width:

240 m 200 m 8 m [maximum] 1.5 m [minimum]

EXPERIMENT

Plot coverage:

64%

Block:

2-G30.3

Block:

Block Length: Block Width: Street Width:

240 m 200 m 8 m [maximum] 1.5 m [minimum] 10 m

Block Length: Block Width: Street Width:

Plot coverage:

55%

Plot coverage:

Height [storey]:

Height [storey

2 [minimum]

15 [maximum]

Density

250 m

201 m

241.6 m2

Building Footprint

Building Footp

65%

Ground solar exposure 15%

Height 15000 m

55%

Ground solar exposure 14%

Ground solar

Height

3137 m

3276 m

ologies and Design

2-G60.15

Block:

3-G100.24

Block:

4-G100.41

240 m 200 m 8m [maximum] 1.5 m [minimum]

Block Length: Block Width: Street Width:

240 m 200 m 8 m [maximum] 1.5 m [minimum]

Block Length: Block Width: Street Width:

240 m 200 m 8 m [maximum] 1.5 m [minimum]

49%

y]:

Plot coverage:

47%

Height [storey]:

2 [minimum]

15 [maximum]

Density

212.2 m

306 m2

Building Footprint

print 49%

exposure

Plot coverage:

47%

Ground solar exposure

22%

56%

Ground solar exposure

23%

19%

Height

2847 m

4045 m

Architectural Association School of Architecture - Emergent Techno

EMERGENCE AND DESIGN SEMINAR Shibam - 16th century Primitie Block:

Block:

1-G1.1

Block:

2-G100.41

Block:

Block Length: Block Width: Street Width:

240 m 200 m 8 m [maximum] 1.5 m [minimum]

Block Length: Block Width: Street Width:

240 m 200 m 8 m [maximum] 1.5 m [minimum] 10 m

Block Length: Block Width: Street Width:

EXPERIMENT

Plot coverage:

64%

Plot coverage:

50%

Plot coverage

Height [storey]:

Height [store

2 [minimum]

15 [maximum]

Density

350 m2

330 m2

342 m2

Building Footprint

Building Foo

65%

Ground solar exposure

50%

Ground solar exposure

15%

14%

Ground solar

Height

15000 m

13008 m

9450 m

ologies and Design

2-G60.35

Block:

3-G100.24

Block:

4-G100.4

240 m 200 m 8m [maximum] 1.5 m [minimum]

Block Length: Block Width: Street Width:

240 m 200 m 8 m [maximum] 1.5 m [minimum]

Block Length: Block Width: Street Width:

240 m 200 m 8 m [maximum] 1.5 m [minimum]

36%

ey]:

otprint

Plot coverage: Height [storey]:

Height [storey]:

2 [minimum]

15 [maximum]

Density

307 m2

305 m2

Building Footprint

36%

r exposure

31%

Plot coverage:

31%

Ground solar exposure

62%

43%

Ground solar exposure

74%

23%

Height

7785 m

10677 m

EMERGENT TECHNOLOGY & DESIGN ARCHITECTURAL ASSOCIATION