Emergencedocumentation , kaushik , sally, sharon, zaqi

EMERGENCE SEMINAR

DOCUMENTATION Emergent Technologies & Design 2015-16 Kaushik Sardesai | Sally Al-Badry | Sharon Ann Philip | Zaqi Fathis

Charles Darwin’s “Tree of Life” [1837].

Source: http://www.colinpurrington.com

Architectural Association School of Architecture Emergent Technologies and Design 2015-16

Charles Darwin’s “I think” Sketch [1837].

Source: https://www.icr.org/article/4404

ABSTRACT

This paper describes the process of exploring evolutionary design techniques in generative algorithms through advanced computation. The concept of “Evo Devo� and the biological process of growth and evolution in living organisms formed the primary basis of this exploration and have been further translated into active simulation in evolutionary computation.The research aims at documenting the process, analysis, strategies and results through the application of the natural principles of growth and development into emergent design techniques and processes.

Keywords: evolution, computation, growth, development

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INTRODUCTION According to Evo-Devo, “the key to understanding form is development, the process through which a single-celled egg gives rise to a complex multi-billion-celled animal.”1

To understand and follow up the entire process, it is essential to know the definition of the key terms used. 2

The entire research is integrated into four main sequences with increasing complexity in terms of their body plan, evolutionary growth and breeding strategy which is backed up with relevant logic, computation tools and techniques.

-“Generations” is the number of iterations in any single run of the simulation.

The experiments are initially based on a primitive geometry, “egg” and is evolved through a series of basic modifiers/operations “genes” to produce a population of individuals. The combination or arrangements of these genes formed the genomes that deliberately highlight a particular feature in each individual of one generation. These individuals are evaluated based on specific fitness criteria, unique to each sequence.

-“Phenotype” is the geometry of the forms that the simulation will produce.

The next generation progressed on the basis of its predecessor through selective ranking. This ranking assisted in determining the possible breeding strategy, for the future generations to achieve optimum fitness in terms 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. This alteration in the DNA sequence leads to increased randomisation of varied individuals and can produce unpredictable results.

-“Population” is the total number of individuals per generation.

-“Gene” is a single parameter that controls the type and intensity of modification to the phenotype.

-“Fitness Criteria” is the criteria on the basis of which the phenotype is evaluated.

-“Mutation” is allocating random modifications to the genes.

-“Mutation Rate” is the frequency of mutation.

-“Mutation Probability” is the probability of a gene to be mutated.

-“Crossover” is the exchange of genes from different phenotypes when breeding to make a descendant.

-“Elitism” is the number of dominant solutions selected to generate the next population.

-“Pareto Front” is when none of the fitness criteria can be improved in value without loss in some of the others. In a population, it is when it is impossible to improve any single individual without degrading another individual.

1. P x. Carroll, S. (2005). Endless forms most beautiful. New York: Norton 2. Source: Architectural Association School of Architecture (EmTech)

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BACKGROUND EVOLUTIONARY COMPUTATION

BODY PLAN & HOMEOBOX

Evolutionary computation is an advancing branch of computer science that explores a series of complex solutions derived from discoveries made in natural biological development of species. Evolutionary computation offers a plethora of research preferences and are mainly based on the biological models generated by renowned scientists like Darwin in “The Origin of Species (1859)” and Carroll in“Endless Forms Most Beautiful (2005)”.

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 expression of the constituent parts of the body that enable researchers to identify and distinguish the morphological differences that have occurred on that organism over a period of time and compare the same. This process gets more complex in terms of evolution as the genome sequences modify and multiply to acclimatize to the present environmental changes and validate its existence.

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 fitnesscriteria.The research initially commenced with standard operations in “Rhinoceros” and further advanced to “Grasshopper” and ultimately “Octopus” to simlate, analyze and achieve a multi-objective optimization within the population.

EVOLUTIONARY DEVELOPMENT (EVO-DEVO) The paradigmatic shift from the Darwinian theory of evolution in 1859, to D’Arcy W. Thompson’s views on growth and form in 1917 to Sean BCarroll in 2005 led to the rise of “Evo-Devo” as an integrated branch of science which witnessed the concatenation of the theories behind evolution and embryological development.

Evo-Devo” literally means Evolutionary Development. This field of biology construes and compares the growth and development of different organisms to ordain a totemic inter-relationship between them. The term “evolution” can be defined as, “ the exchange in genetic code over several generations” and “development” as “the growth of an embryo to adult form”1 .

The term “Homeobox” is defined as a base pair sequence of DNA within a group of homeotic genes, which encode a protein domain called the Homeodomain. The homeotic genes with these homeoboxes are called Hox genes as an abbreviation.2 The homeobox contains two main types of genes, the “activators” and the “repressors”.

These switches enable or restrict the transformation of any gene activity occurring along a particular axis in a genetic expression of an evolving embryo. The activators switch on the gene activity while the repressor cancels out the activators, thereby restricting the development of specific segments in the body plan of the embryo.3 The integration of these switches in the coding of the gene expression plays a significant role in the development of any organism and determines the evolution or mutation of specific body parts in the body plan. Any alteration or modification caused in this integration results in mutation.4 1. P 29-30 Weinstock, M., Hensel, M. and Menges, A. (2010). Emergent technologies and design : towards a biological paradigm for architecture. Oxon: Routledge 2. P 62-63 Carroll, S. (2005). Endless forms most beautiful. New York: Norton.

3. P114- 118 Carroll, S. (2005). Endless forms most beautiful. New York: Norton.

4. P97-102 Weinstock, M. (2010) The Architecture of Emergence. Chichester, UK: Wiley.

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METHODOLOGY AIMS & OBJECTIVES The primary aim of this research is to demonstrate and evolve a series of generations that progress in accordance to multiple criteria of fitness. The concepts and understanding developed from Evo-Devo form the basis of laying the foundations of evolutionary computational decisions.

The secondary aim is an attempt to establish a relationship between the theories of Evolution and embryological development into the architectural realm to generate emergent design techniques 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 comparisons and tests to predict the optimal output are implemented to generate accurate results.

SCOPE & LIMITATIONS 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 limited to a maximum of three fitness criteria and involves rationalizing the primitive in each sequence progression.

Certain limitations based on the population size and number of generations are imposed to interrogate and compare the results within the provided time frame.

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INDEX

All 6 Generations Analysis

SEQUENCE _2

SEQUENCE _1 1.0 1.1 1.2 1.3 1.4 1.5

Introduction Transition Functions Generation_01 Generation_02 Generation_03 Observations

07 08 09 11 13 15

SEQUENCE_3 3.0 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8

Introduction Pseudocode Fitness Criteria Gene Pool Generation_03 Generation_06 Generation_10 Comparison Observations

2.0 2.1 2.2 2.3 2.4 2.5 2.6

Introduction Strategy Generation_4 Generation_5 Generation_6 Mutation Observations

SEQUENCE _4 29 30 31 32 33 35 37 39 40

4.0 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9

Introduction Fitness Criteria Generation_10 Generation_30 Generation_50 Generation_70 Generation_90 Comparison Observations Comparison

5.0 Summary 6.0 Conclusion 7.0 References

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17 18 19 21 23 25 26

43 44 45 49 53 57 61 65 66 67 68 69 70

05

SEQUENCE 01

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1.0 INTRODUCTION

- The Primitive The cube (Fig 7.2)is selected as the primitive geometry for the creation of the individuals in each generation and all of the experiments are conducted on this primitive geometry and evolved through a series of basic modifiers/operations taken from the rhinoceros syntax to produce phenotypes.

The set of genes arranged or combined together forms the genomes that deliberately actuate a series of transformations to make each individual unique and different from the other.

Sequence01

• Length: 10 units • Breadth: 10 units • Height: 10 units • Volume=1000 cubic units • Surface Area=600 square units • Cross sectional Area=100 square units

- Modification Rules In order for the individuals to be equally comparable, a set of modification rules are set as imperative rules to create the individuals.

-The first modification rule is the point of reference in the primitive geometry “the cube” that it should always be the front bottom left vertice of the cube.

(Fig 7.3) -The other modification rule is that the values of the functions are absolute, except for the array modifier in which the value is relative, in this case the spacing should be equivalent to half the length of the face of the parent. (Fig 7.4)

- Fitnes Criteria

• The point of reference for each gene in the production of individuals shall be FRONT>BOTTOM>LEFT • In the Array modifier, the spacing shall be equivalent to HALF THE LENGTH of the face of the parent.

Though the concept of Charles Darwin emphasized greatly on the process of "Natural Selection", D'Arcy Thompson in the book "On Growth and Form" criticizes the same by [1] to not make up speculative stories about natural selection just because a gradual transition can be observed, [2] some changes must be saltational rather than gradual (just as some geometries can only transform into others through a discontinuity.1 For D'Arcy Thompson, the transitions in the first criticism only "reflect a changing set of external forces acting on unaltered biological material".2 These individuals are evaluated based on specific fitness criteria, the first fitness criteria for the first generation is the volume to surface area and the aim is to maximize the volume and minimize the surface area of the individual. (Fig 7.5)

Fig 7.2

Fig 7.3

Fig 7.4 • Maximize Volume

1,2. P xi Thompson, D. and Bonner, J. (1961). On growth and form. Cambridge: Cambridge University Press

Fig 7.5 Emergence Seminar

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1.1TRANSITION FUNCTIONS / GENE POOL

Mx Move in x -plane 5

My Move in y -plane

5

Mz Move in z -plane

As the system is based on the concept of embryological development, the individuals are created using the same growth strategy in the hereditary genotype.

The genepool involves a collection of five Rhinoceros commands (Move, Rotate, Copy, Scale and Array). Each command has the choice of transforming along different axis (x,y,z) or (xyz) at the same time. Every phenotype is created by a combination of six genes to create a specific genome which is applied to the primitive to create a new individual.

5

The values of the transformation are of absolute value except for the “array� command which is relative to the length of the base of the cube.

Cx Copy in x -plane

Cy Copy in y -plane

5

5

Cz Copy in z -plane

5

Rxy Rotate xy -plane 30

Rxz Rotate in xz -plane 30

Ryz Rotate in yz -plane 30

Sx Scale in x -plane *1.3

Sy Scale in y -plane

Sz Scale in z -plane *1.3

Sxyz Scale in xyz -plane *1.3

Az Array in z -plane

Axyz Array in xyz -plane 3 copies

Ax Array in x -plane

3 copies

Ay Array in y -plane

*1.3

3 copies

3 copies

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1.2 GENERATION 1

Sequence01

Fig 9.2 : Gene Frequency The first generation is created by making a random combination of six genes from the genepool to record the results of different combinations as seen in the phenotypes. The main aim is to achieve maximum volume over minimum surface area in the individual.The fittest two individuals in the first generation (G1.2 and G1.7) are selected for breeding to create the second generation. The killing strategy is used to kill the eight least fit individuals. (As shown in Fig. 9.1)

14 12 10

Move M

8

Copy C Rotate R

6

Scale S

4

Array A

2

Fittest

Fig 9.1 : Killing Strategy

G1.01 VOLUME

: 2.17

G1.06 VOLUME

G1.03

G1.02 VOLUME

: 2.46

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VOLUME

: 2.17

: 2.53

VOLUME

VOLUME

: 2.14

G1.09 : 2.10

VOLUME

Scale S

Rotate R

Copy C

Move M

0

G1.05

G1.04

G1.08

G1.07 : 2.12

VOLUME

Array A

VOLUME

: 2.16

G1.10 : 2.12

VOLUME

: 2.17

09

1.2 ANALYSIS

Volume / Surface Area 2.17 2.46 2.17 2.14 2.16 2.12 2.53 2.1 2.12 2.17

Individuals (Ranked) G1.7 G1.2 G1.1 G1.3 G1.10 G1.5 G1.4 G1.9 G1.6 G1.8

Mean Fitness Value Standard Deviation factor

Volume / Surface (Ordered) 2.53 2.46 2.17 2.17 2.17 2.16 2.14 2.12 2.12 2.1

2.21 0.14

Standard Deviatinon Level Values 2.64 2.50 2.36 2.21 2.07 1.93 1.78

Normal Distribution 0.03 0.38 1.69 2.78 1.69 0.38 0.03

2.78

3.00 2.50

Normal Distribution

Individuals G1.1 G1.2 G1.3 G1.4 G1.5 G1.6 G1.7 G1.8 G1.9 G1.10

2.00

1.69

1.69

1.50 1.00

0.38

0.50 0.00

0.03 0.00

0.50

1.00

1.50

0.38 0.03

2.00

2.50

3.00

Standard Deviation Level Values

OBSERVATIONS Generation 01 produced 10 individuals, which are ranked according to their fitness criterion: the ratio of maximum volume over minimum surface area.

The graph was plotted against two values: “Standard deviation levels” on the X-axis and the normal distribution on the Y-axis.

A range of standard deviation “S.D.” level is set from “-3 to +3” to the initial fitness values to determine the average mean. It can be observed and analyzed from the curve that the maximum values are close to the average mean value, as conformed by the low standard deviation factor.

G1.08

G1.07 VOLUME

Fittest

: 2.53

VOLUME

: 2.10

Least Fit

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1.3 GENERATION 2

Sequence01 25

The second generation is created by breeding the two fittest individuals of the first generation. The genome sequence is formed by crossing over the genes of the fittest phenotypes to achieve maximum volume. The fittest individual and the least fit individual in the second generation (G2.1 and G2.10) are selected to create the third generation. The killing strategy is used to kill the other eight individuals between the fittest and the least fit individual. (Fig.11.1)

20 Move M

All 6 Generations Analysis

15

Copy C

The morphological variation in the phenotypes is more distinct across G2.06 to G2.09 and have similar genomes whereas, individual G.02 and G2.03 have differential genome sequences but nearly identical morphology.

Rotate R

10

Scale S

Array A

Fittest

5

Least Fit

Fig 11.1 : Killing Strategy

Array A

G2.01 VOLUME

: 1.90

G2.06 VOLUME

G2.03

G2.02 VOLUME

: 3.24

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VOLUME

: 3.25

VOLUME

: 2.46

: 3.08

VOLUME

VOLUME

Rotate R

Copy C

Move M

0

G2.05

G2.04

G2.08

G2.07 : 3.33

VOLUME

Scale S

: 2.15

VOLUME

: 3.02

VOLUME

G2.09

: 3.40

G2.10 : 3.85

11

1.3 ANALYSIS Volume / Surface Area 1.9 2.33 1.67 1 0.33 -0.33 -1 -1.67 -2.33 -3

Individuals (Ranked) G2.2 G2.1 G2.3 G2.4 G2.5 G2.6 G2.7 G2.8 G2.9 G2.10

Volume / Surface (Ordered) 2.33 1.9 1.67 1 0.33 -0.33 -1 -1.67 -2.33 -3

0.80

0.65 0.65

0.70

Normal Distribution

Individuals G2.1 G2.2 G2.3 G2.4 G2.5 G2.6 G2.7 G2.8 G2.9 G2.10

0.60 0.50

0.42

0.40 0.30

0.17

0.20 0.10

Mean Fitness Value Standard Deviation factor

2.97 0.58

Standard Deviatinon Level Values Normal Distribution 4.70 4.12 3.55 2.97 2.39 1.81 1.24

0.01 0.09 0.42 0.69 0.42 0.09 0.01

0.00

0.01 0.00

0.50

1.00

0.17

0.05 1.50

0.05 2.00

2.50

3.00

3.50

Standard Deviation Level Values

4.00

0.01

4.50

5.00

OBSERVATIONS There is an increase in the Standard Deviation Factor and Mean Fitness Value from the previous generation. This indicates an increase in variation and fitness in succession. The individuals are ranked according to the assigned fitness criteria in decreasing order of fitness and follow the same order for breeding and producing the phenotypes for the next generation.

G1.01

G1.10 VOLUME

0.42

: 3.85

Fittest

VOLUME

: 1.90

Least Fit

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All 6 Generations Analysis

1.4 GENERATION 3

Sequence 01 20

The third generation was created by a cross breeding strategy of breeding the fittest and least fit individuals from the second generation. The individuals were ranked and evaluated in accordance to identical fitness criteria and aimed at achieving greater variation. No mutations were introduced in this generation.

18 16 14

Move M

12

Copy C

10

Rotate R

8

Scale S

6

Array A

Array A

G3.01 VOLUME

: 2.49

G3.06 VOLUME

G3.03

G3.02 VOLUME

: 2.40

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VOLUME

: 2.49

: 2.03

VOLUME

VOLUME

: 2.52

G3.09 : 2.14

VOLUME

2 Scale S

Rotate R

Copy C

Move M

0

G3.05

G3.04

G3.08

G3.07 : 2.18

VOLUME

4

VOLUME

: 2.29

G3.10 : 2.15

VOLUME

: 2.85

13

1.4 ANALYSIS Volume / Surface Area 2.49 2.4 3.49 2.53 2.29 2.18 2.03 2.14 2.15 3.85

Individuals (Ranked) G3.10 G3.1 G3.4 G3.3 G3.2 G3.5 G3.6 G3.9 G3.8 G3.7

Volume / Surface (Ordered) 3.85 3.49 2.53 2.49 2.4 2.29 2.18 2.15 2.14 2.03

0.80

0.65 0.65

0.70

Normal Distribution

Individuals G3.1 G3.2 G3.3 G3.4 G3.5 G3.6 G3.7 G3.8 G3.9 G3.10

0.60 0.50

0.41

0.30

0.17

0.20 0.10

Mean Fitness Value Standard Deviation factor

2.55 0.58

Standard Deviatinon Level Values 4.31 3.72 3.14 2.55 1.97 1.39 0.80

Normal Distribution 0.01 0.09 0.41 0.68 0.41 0.09 0.01

0.00

0.01 0.00

0.50

0.17

0.04

1.00

0.04 1.50

2.00

2.50

3.00

3.50

4.00

0.01 4.50

5.00

Standard Deviation Level Values

OBSERVATIONS A nearly negligible change in the overall standard deviation factor was observed in the graph of generation 03 which indicated stability in the morphological variation of the individual in relation to evaluating the fitness criterion. There was a drop in the mean fitness value as compared to the previous generation, which was the probable outcome of the breeding strategy that was adopted for the current generation.

G2.07

G2.10 VOLUME

0.41

0.40

: 2.85

Fittest

VOLUME

: 2.03

Least Fit

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All 6 Generations Analysis

1.5 OBSERVATIONS

The normal distribution curve graph represents the shift in standard deviation across the three evaluated generations. There is a significant increase in the overall fitness across the generations but limited variation in the last generation 02 and 03. This is probably due to the high degree of initial experimentation and lack of mutation.

The gene frequency line graph shows a substantial displacement in the “array” gene whereas the “rotate” remained stagnant throughout the sequence. It is evident from the data that the gene pool needed expansion in terms of intensity and count to achieve more variation.

Comparative Analysis

3.00

2.50

Normal Distribution

Generation 02 and 03 produced nearly identical phenotypes in terms of their morphology.The sudden change in variation from generation 01 to 02 indicates that the breeding and crossover strategy needs to be revised in order to achieve the possible outcome of controlled variation. The genome length is also an important area of investigation in the initial sequences.

Sequence01

2.00

1.50

1.00

0.50

0.00

0.00

0.50

1.00

1.50

It is also relatively difficult to permutate and combine various breeding strategies in only three generations which increases the similitude in the deviation factor and ultimate results.

2.00 2.50 3.00 Standard Deviatinon Level Values GEN 01

GEN 02

3.50

4.00

4.50

5.00

GEN 03

25

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

20 15

generation 01 generation 02

10

generation 03

5

Array A

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Scale S

Rotate R

Copy C

Move M

0

15

SEQUENCE 02

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2.0 INTRODUCTION

Sequence02

The second sequence was developed from the previous generation of the first sequence, maintaining identical gene pool, intensity and genome formation. In terms of fitness criteria, the ratio of height over width at base was introduced in addition to the initial fitness criterion.The primary reason for this criterion for this criterion was to induce and interrogate the connection of each individual with the physical world in terms of its physical existence. Thus this criterion aimed to achieve greater volume by verticality and minimizing the built footprint at the base.

Body Plan :

4

4 1

1

3 2

3 2

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 expression of the constituent parts of the body that enable researchers to identify and distinguish the morphological differences that have occurred on that organism over a period of time and compare the same. This process gets more complex in terms of evolution as the genome sequences modify and multiply to acclimatize to the present environmental changes and validate its existence.

The concept of body plan is introduced and explored in this sequence to enable and lay emphasis on a growth strategy that could contribute to achieving optimum fitness.

The primitive is divided into four segments as shown in fig.) 1,2,3, and 4 where segment 2 and 4 formed the core of the primitive. All genes in the corresponding genome are assigned to act on specific segments only. This formed the basis behind formulating the growth strategy acting on the body plan. (as shown in fig.17.1).

Fig 17.1 : Body Plan

01.

volume surface area

Fitness Criteria : In order to evaluate and rank the individuals, Darwin's theories of choosing the fittest individuals and removing the unfit was translated into the concept of "Fitness Criteria" are introduced as part of progressing from one generation to the next. To introduce the idea of multi-objective optimisation, another fitness criteria is added in this sequence. The connection of fitness criteria to the body plan plays an important role in defining the possible growth and breeding strategies. The two fitness criteria are: 1- Ratio of volume over surface area : Greater the volume, lesser should be the surface area 2- Ratio of the height to the width of the base: The greater the height. Lesser should be the width of the base.(as shown in fig.17.2)

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02.

height width Fig 17.2 : Fitness Criteria

17

2.1 STRATEGY

Growth Strategy:

3

4

Since the main aim of this sequence is to grow vertical in the Z axis, greater emphasis is given to the core of the primitive i.e. segment 2 and 4. The genome length is divided into a ratio of 1:2:2:1 where, two genes act on each segment of the core respectively.

The outer segments which occupy a greater surface area at the base, transform in response to one gene only.

1

For the successive generations, a 50-50 cross breeding strategy is adopted where the genomes of the selected individuals “parents� produce two hybrid genomes that generate two distinct phenotypes.

Breeding Strategy: The breeding strategy adopted for this sequence is to breed the fittest individual with the five fittest individuals in decreasing order of fitness. The individuals are ranked according to the fittest in both the fitness criteria respectively. Thus by following the logic of crossing over the genome sequence, five individuals produce ten phenotypes every successive generation . (as shown in fig.18.4). The killing strategy is used to eliminate the four least fit individuals from the evaluated generation.

1

2

Fig 18.1 : Body Plan

2

2

4

4

3

Fig 18.2 : Gene Sequence

50% 50% Fig 18.3 : Growth Strategy

02

01

01

02

04

03

03

04

05

06

06

05

07

08

07

09

08

09

10

10

Fig 18.4 : Breeding Strategy

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2.1 GENERATION 4

Sequence02

Generation 04 is the first generation produced in sequence 02 from a random combination of six genes from the gene pool. In terms of the morphological variation, every alternating phenotype is found to be almost identical. This is probably due to the limited intensity assigned to the genes and their arrangement in the genome which resulted in similar phenotypes.

Thus to induce a possible incrementation in the fitness and variation, the individuals are ranked in order of their fitness values and the fittest five individuals are selected to produce the next generation over a single point crossover strategy.

20 18 16 14

Move M

12

Copy C

10

Rotate R

8

Scale S

6

Array A

4 2 Array A

G4.01

G4.02

G4.03

G4.04

V.SA. : 2.11 H.W. : 1.8

V.SA. : 1.59 H.W. : 1.68

V.SA. : 2.00 H.W. : 1.59

V.SA. : 1.3 H.W. : 1.68

G4.06

G4.07

G4.08

G4.09

V.SA. : 2.15 H.W. : 1.29

V.SA. : 2.00 H.W. : 1.44

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V.SA. : 1.44 H.W. : 1.30

V.SA. : 1.39 H.W. : 0.9

Scale S

Rotate R

Copy C

Move M

0

G4.05 V.SA. : 1.52 H.W. : 1.27

G4.10 V.SA. : 1.99 H.W. : 1.62

19

2.1 ANALYSIS

Standard Deviatinon Level Values 2.69 2.37 2.06 1.75 1.44 1.12 0.81

Individuals (Ranked) G4.6 G4.1 G4.3 G4.7 G4.10 G4.2 G4.5 G4.8 G4.9 G4.4

Normal Distribution 0.01 0.17 0.77 1.28 0.77 0.17 0.01

Mean Fitness Value Standard Deviation factor

Volume / Surface (Ordered) 2.15 2.11 2 2 1.99 1.59 1.52 1.44 1.39 1.3

1.40 1.75

1.20 Normal Distribution

Volume / Surface Area 2.11 1.59 2 1.3 1.52 2.15 2 1.44 1.39 1.99

1.00 0.80

1.44

2.06

0.60 0.40 0.20 0.00

1.749 0.312872178

1.12 0.00

0.81 1.00

0.50

2.37 2.69

1.50 2.00 Standard Deviatinon Level Values

2.50

Individuals

Height / Width

Individuals (Ranked)

Height / Width (Ordered)

G4.1 G4.2 G4.3 G4.4 G4.5 G4.6 G4.7 G4.8 G4.9 G4.10

1.12 1.45 1.21 1.45 1.45 1.2 1.2 1.45 1.17 1.12

G4.2 G4.4 G4.5 G4.8 G4.3 G4.6 G4.7 G4.9 G4.1 G4.10

1.45 1.45 1.45 1.45 1.21 1.2 1.2 1.17 1.12 1.12

3.00 1.24

1.33

2.50

2.00 1.14

1.42

1.50

1.00 1.52

1.05

Standard Deviatinon Level Values Normal Distribution

1.70 1.56 1.42 1.28 1.14 1.00 0.86

0.03 0.39 1.73 2.85 1.73 0.39 0.03

3.00

Gen 04 - Height / Surface Area

Normal Distribution

Individuals G4.1 G4.2 G4.3 G4.4 G4.5 G4.6 G4.7 G4.8 G4.9 G4.10

0.50 1.61

0.95 0.00

0.86 0.00

0.20

0.40

0.60

0.80 1.00 Standard Deviatinon Level Values

1.70 1.20

1.40

1.60

1.80

Gen 04 - Height / Width

Mean Fitness Value Standard Deviation factor

1.28 0.14

Kaushik Sardesai | Sally Al-Badry | Sharon Ann Philip | Zaqi Fathis

20

2.3 GENERATION 5

Sequence02

The single point crossover strategy devised in generation 04 to produce generation 05 resulted in a slight increase in the morphological variation. A steady rise in the mean fitness values of both criteria is observed. This is due to the killing strategy that eliminated the least fit individuals from the previous generations, thus the genome sequences of those phenotypes were not carried forward in the subsequent generations.

25 20 Move M

15

Copy C Rotate R

10

Scale S Array A

5

Array A

G5.01

G5.02

G5.03

G5.04

V.SA. : 1.97 H.W. : 1.12

V.SA. : 1.39 H.W. : 1.58

V.SA. : 2.11 H.W. : 1.12

V.SA. : 2.00 H.W. : 1.24

G5.06

G5.07

G5.08

G5.09

V.SA. : 1.39 H.W. : 1.58

V.SA. : 2.00 H.W. : 1.50

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V.SA. : 1.48 H.W. : 1.58

V.SA. : 1.12 H.W. : 1.24

Scale S

Rotate R

Copy C

Move M

0

G5.05 V.SA. : 1.2 H.W. : 1.50

G5.10 V.SA. : 2.00 H.W. : 1.92

21

2.3 ANALYSIS

Standard Deviatinon Level Values 2.68 2.40 2.12 1.84 1.56 1.28 1.00

Individuals

G5.1 G5.2 G5.3 G5.4 G5.5 G5.6 G5.7 G5.8 G5.9 G5.10

Height / Width

1.12 1.58 1.12 1.24 1.12 1.58 1.50 1.58 1.24 1.24

Standard Deviatinon Level Values

1.91 1.72 1.53 1.33 1.14 0.95 0.75

Normal Distribution 0.02 0.19 0.86 1.42 0.86 0.19 0.02

Individuals (Ranked) G5.9 G5.3 G5.4 G5.10 G5.1 G5.5 G5.7 G5.8 G5.2 G5.6

Volume / Surface (Ordered) 2.12 2.11 2.00 2.00 1.97 1.97 1.97 1.48 1.39 1.39

Mean Fitness Value Standard Deviation factor

Individuals (Ranked)

1.84

1.40 1.20 1.00 1.56

0.80

2.12

0.60 0.40 0.20 0.00

1.28 0.00

0.50

1.00 1.00

2.40 2.68 1.50

2.00

2.50

3.00

Standard Deviatinon Level Value

1.84 0.28

Gen 05- Volume / Surface Area

Height / Width (Ordered)

G5.2 G5.6 G5.8 G5.7 G5.4 G4.6 G5.9 G5.10 G5.1 G5.5

1.58 1.58 1.58 1.50 1.24 1.24 1.24 1.12 1.12 1.12

2.50

1.33

2.00

1.50 1.14

1.53

1.00

0.50 0.95 0.00

1.72

0.75 0.00

0.50

1.91 1.00

1.50

2.00

2.50

Standard Deviatinon Level Values Gen 05 - Height / Width

Normal Distribution

0.02 0.28 1.25 2.07 1.25 0.28 0.02

1.60

Normal Distribution

Volume / Surface Area 1.97 1.39 2.11 2.00 1.97 1.39 1.97 1.48 2.12 2.00

Normal Distribution

Individuals G5.1 G5.2 G5.3 G5.4 G5.5 G5.6 G5.7 G5.8 G5.9 G5.10

Mean Fitness Value Standard Deviation factor

1.332 0.193121723

Kaushik Sardesai | Sally Al-Badry | Sharon Ann Philip | Zaqi Fathis

22

2.4 GENERATION 6

Sequence02 30

Generation six is generated by cross breeding the five fittest individuals of the fifth generation. The resulted individuals have two identical twins in their genome and phenome (G6.04, G6.07,G6.09) and (G6.08, G6.10). The fitness criteria to evaluate the individuals of this generation are the same as the criteria in Generations four and five.

The absence of a mutation strategy in all generations of this sequence is one of the primary reason of the production of twin phenotypes in each generation. The permutations and combinations of genome sequences and limitation of its length show signs of a probable stagnancy throughout this experiment.

25 20

Move M Copy C

15

Rotate R Scale S

10

Array A

5

Array A

G6.01

G6.02

G6.03

G6.04

V.SA. : 1.97 H.W. : 1.21

V.SA. : 2.15 H.W. : 1.21

V.SA. : 1.72 H.W. : 1.58

V.SA. : 2.00 H.W. : 1.73

G6.06

G6.07

G6.08

G6.09

V.SA. : 2.12 H.W. : 1.21

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V.SA. : 2.00 H.W. : 1.73

V.SA. : 1.68 H.W. : 1.78

V.SA. : 2.00 H.W. : 1.21

Scale S

Rotate R

Copy C

Move M

0

G6.05 V.SA. : 2.11 H.W. : 1.12

G6.10 V.SA. : 1.68 H.W. : 1.78

23

2.4 ANALYSIS

Standard Deviatinon Level Values 2.46 2.29 2.12 1.94 1.77 1.60 1.42

Individuals

G6.1 G6.2 G6.3 G6.4 G6.5 G6.6 G6.7 G6.8 G6.9 G6.10

Standard Deviatinon Level Values

2.10 1.86 1.63 1.40 1.16 0.93 0.70

Mean Fitness Value Standard Deviation factor

Normal Distribution 0.03 0.31 1.40 2.30 1.40 0.31 0.03

Height / Width

1.21 1.21 1.58 1.73 1.12 1.21 1.34 1.58 1.21 1.78

Individuals (Ranked) Volume / Surface (Ordered) G6.2 2.15 G6.6 2.12 G6.5 2.11 G6.4 2.00 G6.7 2.00 G6.9 2.00 G6.1 1.97 G6.3 1.72 G6.8 1.68 G6.10 1.68

1.94 2.00

1.94 0.17

1.50

1.77

2.12

1.00

0.50 1.60 0.00

2.29

1.42 0.00

0.50

1.00

2.46

1.50

2.00

2.50

3.00

Standard Deviation Level Values

Individuals (Ranked)

Height / Width (Ordered)

G6.10 G6.4 G6.3 G6.8 G6.7 G6.6 G6.1 G6.2 G6.9 G6.5

1.78 1.73 1.58 1.58 1.34 1.21 1.21 1.21 1.21 1.12

Normal Distribution

0.02 0.23 1.04 1.71 1.04 0.23 0.02

2.50

Normal Distribution

Volume / Surface Area 1.97 2.15 1.72 2.00 2.11 2.12 2.00 1.68 2.00 1.68

Gen 06 - Volume / Surface Area

1.80 1.40 1.60 1.40 Normal Distribution

Individuals G6.1 G6.2 G6.3 G6.4 G6.5 G6.6 G6.7 G6.8 G6.9 G6.10

1.20 1.16

1.00

1.63

0.80 0.60 0.40 0.93

0.20 0.00

Mean Fitness Value Standard Deviation factor

1.397 0.233325952

1.86

0.70 0.00

0.50

2.10 1.00

1.50

2.00

2.50

Fitness Value (Converted) Gen 06 - Height / Width

Kaushik Sardesai | Sally Al-Badry | Sharon Ann Philip | Zaqi Fathis

24

2.5 MUTATION The term “Mutation” is a result of any natural change or alteration caused in the genetic expression of the genome. It can be further sub-divided into different categories namely, “deletion, duplication, inversion, insertion and trans-location”.

Mutation serves as a means of identifying the effect and impact of the corresponding allele1 on the organism and is visible through its morphogenetic characteristics.2 In order to evaluate and understand the affect of mutation techniques and compare the results, different mutation strategies are applied to the identical individuals in the sixth generation.

The first tested technique is “deletion” applied to individual (G6.04) which caused in decreasing the values of both fitness criteria but produced a new morphological variation in the offspring which was absent in almost all the phenotypes of generations 4, 5 and 6. Thus it is evident, that reconfiguring the genome length in terms of either insertion or deletion can affect the body plan significantly.

Sequence02

Deletion: G6.4 1

2

2

4

4

1

2

2

4

4

3

G6.4 V.SA. = 2.00 H.W. =1.50

Duplication: G6.7 1

2

2

4

1

2

2

2

4

4

3

4

3

G6.7

“Duplication” is applied to two distinct individuals (G6.07 & G6.10) to test the differences in the outcome. A nearly negligible difference is observed in the standard deviation values and variation. This is mainly due to the repetition of a particular gene in the genome sequence.

Thus it can be concluded that duplicating genes of limited intensity will produce nearly identical phenotypes with negligible deviation in the fitness criteria, irrespective of their position in the genome sequence. It thus suggests the possibility of [1]Increasing the gene count in the gene pool [2] Selective deifferentiation in the intensity of the genes [3] Redefining the length of the genome.

1

2

1

2

2

2

4

2

4

Emergence Seminar

Emergent Technologies and Design 2015-16

V.SA. = 1.79 H.W. = 1.21

4

3

4

3

G6.10

1

2. P97-102 Weinstock, M. (2010) The Architecture of Emergence. Chichester, UK: Wiley.

V.SA. = 2.00 H.W. = 1.50

Duplication: G6.10

Allele is a possible form of gene located in the specific coordinate in the specific chromosome.

1. Bailey, R. and Bailey, R. (2016). How Alleles Determine Traits. [online] Available at: http://biology.about.com/od/geneticsglossary/g/alleles.htm [Accessed 10 Feb. 2016].

V.SA. = 1.72 H.W. = 1.21

2

2

4

4

3

V.SA. =1.68 H.W. =1.78

V.SA. =1.62 H.W. =1.76

3

V.SA. =1.66 H.W. =1.74

25

2.6 OBSERVATIONS

The normal distribution curve graph for this sequence shows an incremental rise and shift in the standard deviation levels across the three generations. In the first fitness criteria of Volume over Surface Area, the progression represents an increase in fitness but decrease in variation. The second fitness criteria of greater height over width of the base, represents a steady increase both in fitness and variation.

1.94 2.00

Normal Distribution

Both the graphs indicate that the growth strategy in relation to the Body Plan needs to be reconfigured and a logical strategy of “Fate Maps” 1 need to be defined in advance. A unique set of “Hox” 2 genes needs to be directed in the gene pool that can evolve particular segments of the Body Plan as per the growth strategy.

VOLUME OVER SURFACE AREA COMPARISON 2.50

The gene frequency line graph shows an increase in the frequency of the “array” gene whereas the “scale” remained stagnant throughout the sequence. The homogeneity in the frequency of the other genes throughout this sequence is mainly informed by the contradicting fitness criteria that demanded vertical growth of the body to achieve the maximum fitness. Thus most of the genes remained dormant as they could not contribute to evolving the desired morphology.

1.50

1.771.84 1.75

2.12

1.00 1.56

1.44

2.12 2.06

0.50 1.12 0.00

0.81 0.00

0.50

2.29

1.42

1.00 1.00

1.50 Fitness Value (Converted)

Gen 04_Final

1. The term “Fate Map” are maps that define the sequence and function of the genes in the genome.

2. Hox genes, also known as the Homeobox genes, are the controller genes in the gene pool and produces active genes, that act on a specific location in the body.

1.60

1.28

Gen 05_Final

2.40 2.37 2.46

2.00

2.68 2.69

2.50

3.00

Gen 06_Final

HEIGHT OVER WIDTH COMPARISON 3.00 1.28

30 25 20 generation 04

Normal Distribution

2.50 1.33

2.00 1.14

1.42 1.40

1.50 1.14

1.53

1.16

1.00

1.63

15

generation 05 generation 06

10 5

0.50

0.00

1.00 0.95 0.93 0.700.75 0.00

0.50

1.56

0.86

1.72 1.70

1.00

1.50

1.86 1.91 2.00

2.10 2.50

Fitness Value (Converted)

Array A

Scale S

Rotate R

Copy C

Move M

0

Gen 04_H_W

Gen 05_H_W

Gen 06_H_W

Kaushik Sardesai | Sally Al-Badry | Sharon Ann Philip | Zaqi Fathis

26

Emergence Seminar

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27

SEQUENCE 03

28

3.0 INTRODUCTION

Sequence03

KOWLOON WALLED- CITY1 The Kowloon walled city was like a glitch in the urban fabric of Hong Kong. Densely developed plot of 2.7 hectare block of unrestrained city was a tight knit community. Last century it was considered to be the most densely populated place on earth with 3,250,000 people per square mile, compared to Hong Kong which was merely 17,000p/sq. mile. The site was originally a Chinese Military outpost which was later leased to the British Government in 1898.

Soon after China announced its intentions to reclaim the land and open it for the refugees.

The vast complex of buildings mostly ranged from 10 storied to 14 storied tall and the alleyways which made their way through this dense labyrinth measured merely 1-2 meters in width. The under-maintenance of the site and major flaws in planning converted the site into an unsanitary, claustrophobic disaster.

The development and construction of the city preceded without public control, thus, most of the 350 buildings exhibit poor foundations and utilities. Due to the very limited amount of space, the apartments extended into caged balconies and rooftop extensions. Despite its dystopian appearance, extreme poverty and unsanitary conditions residents remember the place very fondly. The walled city was demolished in 1993 due to unacceptable sanitary conditions.

2

BODY PLAN As a part of our experiments, Sequence 03 basically emphasized on testing the evolution of this configuration as an urban block with multiple fitness criteria. The evaluation strictly pertains to a criteria based on external environmental pressures in addition to two other fitness criteria that are physically explicit in the architectural realm.

The block footprint is treated as the initial blueprint of the body plan. It is divided into 5 segments namely A/B/C/D/E where AB and CD are equal in linear dimension.(Fig. 29.1) Segment E is independently unique in its dimension.

As the cell density of Kowloon city was extremely high and was mainly housing, the body plan is so divided according to areas of each cell. Thus, the biggest units are in segment AB, the smallest units in CD and the average of both in segment E respectively. The linear divisions relieve the density of the massing without compromising on the internal areas. The body plan is a relatively uncomplicated intervention and can be implemented in any other urban block of similar density.

1.Rickard, N.(2016). Life Inside The Kowloon Walled City. Arch Daily- Infographic.

2.3. Owen, P. (2012). Kowloon Walled City: A rare insight into one of the most densely populated places on earth which housed 50,000 people. [online] Mail Online. Available at: http://www.dailymail.co.uk/news/article-2139914/A-rare-insight-Kowloon-Walled-City.html [Accessed 10 Feb. 2016].

3

B

A

C

D

E (Fig. 29.1)

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29

3.1 PSEUDOCODE

GENERATION

MUTATION PROBABILITY

MUTATION RATE

CROSSOVER RATE

0.0

0.0

0.5

0.5

0.3

0.3

0.5

0.5

0.5

0.5

0.5

0.5

ELITISM

G0 G1

Step 01

Step 02

Step 03

G2 G3

G4 G5

Step 06

Step 05

Step 04

1. Define the length of the plot (default 120.0 m) 2. Define the width of the plot (default 100.0 m) 3. Define the building height (default 3.0 m) 4. Generate the plot rectangle with the given dimension (100m/120m) 5. Divide the plot rectangle as per the devised Body Plan 6. Offset the subdivided border by street width Divide plot Outline in grid as per sub segment division.

7. Move the block grid n number by 1 unit height [10<n<14] 8. For each floor in floor levels: Find the floor centre and scale block outline, by random x & y values between (0.8,1.2) 9. Copy in z direction by one floor value. Count: a random number within a user defined range.

Extrude all apartment floors by unit height.

G6

G7 G8 G9 G10

Pseudocode: Re-written from the Architectural Association [EmTech] Emergence Seminar Design Brief.

Kaushik Sardesai | Sally Al-Badry | Sharon Ann Philip | Zaqi Fathis

30

3.2 FITNESS CRITERIA To iterate the generations in this sequence, the idea of multi- objective optimization is adopted where the fitness criteria were revised to establish a relation with the external changes in the environmental pressure. 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.

Sequence03 Criterion 01 Maximum Volume

FITNESS CRITERIA 01 The first fitness criterion aims at maximizing the total volume of the individual.It is defined as such to interrogate the relationship between the total massing of the individual and the internal spatial area available to maximize it to its extent.

A total of 11 gene pools; 5 of scale, 5 of array and 1 of offset are assigned to achieve the optimum fitness in terms of maximizing the volume. The maximum volume that is achievable hypothetically without any other contradicting fitness criteria would be: setting the maximum values for each gene within the defined domain of scale and array and the minimum value in offset.

FITNESS CRITERIA 02 The second fitness criterion is to maximize the ground exposure, which contradicts the first criterion of maximizing volume. This criterion aims at creating internal open spaces between the dense packing of the cells and is controlled by 6 gene pools; 5 of scale and 1 of offset. The fittest individual to achieve this criterion would be the one with maximum offset value of 2.0 and minimum scale value of 0.8 respectively.

FITNESS CRITERIA 03 The third fitness criterion is to minimize the building exposure with respect to a single solar vector. The vector is limited to one point to achieve uniformity in comparing the results. This criterion is calculated on the basis of the surface area of faces exposed to the vector and is simulated by randomizing the numerical values of 11 gene pools; equivalent to the ones assigned to fitness criteria 01.

Emergence Seminar

Emergent Technologies and Design 2015-16

fittest Individual

Least fit Individual

fittest Individual

Least fit Individual

fittest Individual

Least fit Individual

Criterion 02 Maximum Ground Exposure

Criterion 03 Minimum Building Exposure

31

3.3 GENE POOL

ARRAY A domain of 10 to 16 is set for the array modifier with a total gene count of 82 genes. The body plan consists of five segments namely A/B/C/D and E. Each segment is further subdivided into smaller sub segments with 20 sub segments each in A and B, 15 each in C and D and 12 sub segments in E, adding up to a total of 82 sub segments. Thus, there is a total of five gene pools, titled N1/N2/N3/N4/N5 with 20/20/15/15/12 acting on A/B/C/D/E respectively.

OFFSET A domain of 1.0 to 2.0 is set for the offset modifier with a gene count of 4 genes.

The offset mainly focuses on increasing the width of the linear divisions between the body parts in the urban block. The gene count is 4, as each gene acts independently on the linear sub-division but consists of only one gene pool through the entire sequence.

10 Floors

15 Floors

A C

D

12 Floors

E

B

Scale(S):

0.8

1.0

1.2

A C

D E

B

Offset(O):

In the urban block configuration, all modifiers are translated into an entity with architectural relevance. Thus “scale” represents the volumetric expression of each unit, the “offset” focuses on the street widths that are the linear divisions of the segments and the “array” pertains to the number of floors in each sub segment.

All values are taken in “meters” for the sake of comparable analysis and relation with the architectural idea of scale and proportion.

20 Floors

16 Floors

S1 | S2 | S3 | S4 | S5

SCALE A domain of 0.8 to 1.2 is set for the scale modifier with a gene count of 259 genes. Each gene acts on a singular unit after the extrusion of the body parts, the total adding up to 259 such units. One gene pool is assigned to each of the body parts, thereby five gene pools S1/S2/S3/S4/S5 act on five body parts. The numerical value of the genes in each gene pool is randomized to 100% before the first simulation.

N1 | N2 | N3 | N4 | N5

Array in Z direction

0.0

1.0

2.0

O1 | _ | _ | _ | _

The GENE POOL is devised in coherence with the fitness criteria and structure of the body plan. The primary modifiers in this sequence are “scale”, “array (Z-axis)” and “offset”. 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:

A C B

D E

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32

3.4 GENERATION 03

Sequence03

The simulation commenced with the initial parameters set as shown in Fig 32.1 to produce four generations G0/G1/G2/G3 with a population of 10 individuals per generation. The individuals in the third generation are evaluated and ranked according to the absolute value of each individual. The elitism and crossover rate is set to 0.5 to observe the differentiation produced in the generations as it increases the possibility of achieving higher number of dominant solutions over greater exchange of genes from different phenotypes.

ELITISM

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

MUT. PROBABILITY

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

MUT. RATE

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

CROSSOVER RATE

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

G3.01

G3.02

G3.03

G3.04

G3.05

BUILDING VOLUME : 0.64 GROUND EXPOSURE : 0.43 BUILDING EXPOSURE : 0.29

BUILDING VOLUME : 0.24 GROUND EXPOSURE : 0.40 BUILDING EXPOSURE : 0.77

BUILDING VOLUME : 0.45 GROUND EXPOSURE : 0.0 BUILDING EXPOSURE : 0.59

BUILDING VOLUME : 0.12 GROUND EXPOSURE : 0.95 BUILDING EXPOSURE : 0.79

BUILDING VOLUME : 0.0 GROUND EXPOSURE : 0.76 BUILDING EXPOSURE : 1.0

G3.06

G3.07

G3.09

G3.10

BUILDING VOLUME : 0.17 GROUND EXPOSURE : 0.67 BUILDING EXPOSURE : 0.70

BUILDING VOLUME : 0.44 GROUND EXPOSURE : 0.47 BUILDING EXPOSURE : 0.35

BUILDING VOLUME : 0.29 GROUND EXPOSURE : 0.29 BUILDING EXPOSURE : 0.75

BUILDING VOLUME : 0.38 GROUND EXPOSURE : 0.61 BUILDING EXPOSURE : 0.40

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G3.08 BUILDING VOLUME : 0.65 GROUND EXPOSURE : 0.88 BUILDING EXPOSURE : 0.11

33

3.4.1 GENERATION 03 -ANALYSIS

Normal Distribution

As observed in the Delaunay Graph, the simulation produced 16 phenotypes in generation 03. Only the fittest 10 are selected and ranked to facilitate uniform comparison with the future generations. Though the population count is set to a maximum of 10, it is computationally difficile to control the number of phenotypes the simulation will produce.

The convergence graph shows no signs of increase or decrease in fitness across the generation which indicates the need of introducing an effective mutation strategy as both the rate and probability are set to 0.0 in this simulation.

DELAUNAY GRAPH Fitness Value (converted)

Volume Ground Exposure Building Exposure

CONVERGENCE GRAPH

Kaushik Sardesai | Sally Al-Badry | Sharon Ann Philip | Zaqi Fathis

34

3.5 GENERATION 06

Sequence03

On the basis of observations deduced from the previous analysis, the mutation rate and probability is set to an equivalent of 0.3 to induce variation in the phenotypes and test the possibility of convergence, which was quite ambitious.

Similar methodology of ranking the individuals is adopted to test the survival of the fittest phenotype.

The simulation is limited to three generations in this experiment G4/G5/G6 and the individuals of generation 06 are evaluated accordingly. The phenotypes exhibit slight differences in the morphological variation due to induced mutation.

ELITISM

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

MUT. PROBABILITY

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

MUT. RATE

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

CROSSOVER RATE

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

G6.01

G6.02

G6.03

G6.04

G6.05

BUILDING VOLUME : 0.45 GROUND EXPOSURE : 0.71 BUILDING EXPOSURE : 0.34

BUILDING VOLUME : 0.45 GROUND EXPOSURE : 0.34 BUILDING EXPOSURE : 0.43

BUILDING VOLUME : 0.42 GROUND EXPOSURE : 0.46 BUILDING EXPOSURE : 0.34

BUILDING VOLUME : 0.35 GROUND EXPOSURE : 0.47 BUILDING EXPOSURE : 0.60

BUILDING VOLUME : 0 GROUND EXPOSURE : 0.58 BUILDING EXPOSURE : 1.0

G6.06

G6.07

G6.08

G6.09

G6.10

BUILDING VOLUME : 0.57 GROUND EXPOSURE : 0.67 BUILDING EXPOSURE : 0.46

BUILDING VOLUME : 0.26 GROUND EXPOSURE : 0.27 BUILDING EXPOSURE : 0.77

BUILDING VOLUME : 0.34 GROUND EXPOSURE : 0.27 BUILDING EXPOSURE : 0.78

BUILDING VOLUME : 0.06 GROUND EXPOSURE : 1.0 BUILDING EXPOSURE : 0.87

BUILDING VOLUME : 0.45 GROUND EXPOSURE : 0.37 BUILDING EXPOSURE : 0.40

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35

3.5 GENERATION 06 - ANALYSIS

Normal Distribution

According to the logic of our breeding strategy, the presumption of a possible convergence proved to be true in this evaluation which promoted the experiment of introducing further mutation in the next generations.

There is a very slight change in the mean fitness value and standard deviation favtor across 6 generations.

DELAUNAY GRAPH

Fitness Value (converted)

Volume Ground Exposure Building Exposure

CONVERGENCE GRAPH

Kaushik Sardesai | Sally Al-Badry | Sharon Ann Philip | Zaqi Fathis

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3.6 GENERATION 10

Sequence03

To test the possibility of convergence in the progression, the breeding strategy is redefined with an increase in the mutation rate and probability. The simulation run for four generations G7/G8/G9/G10 and generation 10 is evaluated respectively. It is observed from the radar charts that there is significant increase in variation and deviation in the mean fitness values of each fitness criteria.

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 succession. It basically denotes a possibility of neutrality in the fitness values and decreases the probability of producing fitter individuals.

ELITISM

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

MUT. PROBABILITY

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

MUT. RATE

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

CROSSOVER RATE

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

G10.01

G10.02

G10.03

G10.04

G10.05

BUILDING VOLUME : 0.66 GROUND EXPOSURE : 0.54 BUILDING EXPOSURE : 0.0

BUILDING VOLUME : 0.0 GROUND EXPOSURE : 0.51 BUILDING EXPOSURE : 0.95

BUILDING VOLUME : 0.21 GROUND EXPOSURE : 0.0 BUILDING EXPOSURE : 0.97

BUILDING VOLUME : 0.12 GROUND EXPOSURE : 0.65 BUILDING EXPOSURE : 1.0

BUILDING VOLUME : 0.55 GROUND EXPOSURE : 0.27 BUILDING EXPOSURE : 0.27

G10.06

G10.07

G10.09

G10.10

BUILDING VOLUME : 1.0 GROUND EXPOSURE : 0.1 BUILDING EXPOSURE : 0.0

BUILDING VOLUME : 0.73 GROUND EXPOSURE : 0.49 BUILDING EXPOSURE : 0.07

BUILDING VOLUME : 0.21 GROUND EXPOSURE : 0.62 BUILDING EXPOSURE : 0.77

BUILDING VOLUME : 0.65 GROUND EXPOSURE : 0.15 BUILDING EXPOSURE : 0.42

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G10.08 BUILDING VOLUME : 0.42 GROUND EXPOSURE : 1.0 BUILDING EXPOSURE : 0.47

37

3.6.1 GENERATION 10 - ANALYSIS

Normal Distribution

According to our breeding logic and mutation strategy, the graph shows an increase in possibility of achieveing convergence as compared to the previous six generations. The normal distribution curve graph below shows increased variation in the fitness values across the generations.

DELAUNAY GRAPH

Fitness Value (converted)

Volume Ground Exposure Building Exposure

CONVERGENCE GRAPH

Kaushik Sardesai | Sally Al-Badry | Sharon Ann Philip | Zaqi Fathis

38

3.7 PHENOTYPE COMPARISON

Sequence03

OVERALL

GROUND EXPOSURE

G10.05 = 1.0

G06.09 = 1.93

G03.03 = 0

FITTEST INDIVIDUAL

LEAST FIT INDIVIDUAL

FITTEST INDIVIDUAL

G10.03 = 0

G06.05 = 0

G10.02 = 0

FITTEST INDIVIDUAL

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G10.08 = 1.0

LEAST FIT INDIVIDUAL

BUILDING EXPOSURE

BUILDING VOLUME

G03.05 = 0

G06.09 = 1.0

G10.06 = 1.0

G10.06 = 0.1

G03.05 = 1.0

LEAST FIT INDIVIDUAL

FITTEST INDIVIDUAL

LEAST FIT INDIVIDUAL

39

3.8 OBSERVATIONS

GENERATION 03

Normal Distribution

GENERATION 06 GENERATION 10

Mean Fitness Value Generation 03 : 1.47 Generation 06 : 1.45 Generation 10 : 1.38

Average Fitness Value

As an overall comparison, it is observed that generation 03 and generation 06 express similar values of fitness. However, from generation 06 to generation 10, the population gets fitter with an increase in variation. The possible reason for these results is the strongly contradicting fitness criteria of maximising volume and ground exposure which is relatively difficult to optimise in a dense configuration. The rate of elitism and crossover rate is 0.5 throughout the entire simulation and further permutation is required in terms of redefining the elitism and crossover rate across subsequent generations.

Significant exploration in terms of redefining the body plan and increasing the domain of numerical values in the gene pool may result in morphological variations as it was complex to record the evolution and quantify the data of each individual.

Kaushik Sardesai | Sally Al-Badry | Sharon Ann Philip | Zaqi Fathis

40

3.9 PHENOTYPES

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Sequence03

41

SEQUENCE 04

Kaushik Sardesai | Sally Al-Badry | Sharon Ann Philip | Zaqi Fathis

42

4.0 INTRODUCTION

Sequence04

y1

Sequence 04 is advancement from Sequence 03 by aggregating and combining the nominated urban block to form a super block of 16 blocks. The superblock is simplified in this particular sequence to an aggregation of 8 blocks (Fig. 43.2) to reduce the computational load on the simulation.

The simulation is set to run a total of 90 generations with a population count of 10 individuals per generation. The offspring of generation 10,30,50,70,and 90 are evaluated and breeding strategies are experimented with, accordingly. The body plan is modified from the previous generation following the aggregation.

x

x

y1

Fig 43.1

Fig 43.2

All data is analyzed and documented on basis of its absolute values. The units of measurement is determining the divisions and extrusions is in meters for the sake of architectural relevance and calculation.

Fig 43.3

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y3

GENERATION

In the previous sequence, the Body Plan comprised of five segments in which segment E was unique in dimension. However, in this sequence, the dimensions of segments C, D and E are identical to observe, compare and calculate the values accordingly. The area of the sub segments is equal in dimension with differential extrusions. The linear sub-divisions now form the primary and secondary axis of the superblock with “X-X” being the primary vertical and y1, y2,y3 being the primary horizontal axis respectively. (Fig. 43.1) The secondary axis divides the block into five distinct segments as shown in Fig 43.3 according to the new rationale.

The main ambition in sequence 03 was to release the density of the individual cells of the “Kowloon walled city”. The same ambition is translated here by introducing urban pockets at random intervals along the primary and secondary axis of the body. The dimensions of these pockets are controlled via separate domains of different range in order to maintain the consistency in maximizing volume.

y2

y2

y3

G0 G1 G2 G3 G4 . . . . .

G10 G11 G12 G13 G14 . . . . .

G30 G31 G32 G33 G34 . . . . .

G50 G51 G52 G53 G54 . . . . .

G70 G71 G72 G73 G74 . . . . .

G90

MUTATION PROBABILITY

0.5

MUTATION RATE

0.25

CROSSOVER RATE

ELITISM

0.3

0.3

0.5

0.5

0.5

0.5

0.25

0.75

0.5

0.75

0.25

0.75

0.5

0.75

0.2

0.5

0.5

0.5

43

4.1 FITNESS CRITERIA The fitness criteria for this sequence are identical to the criteria of the previous sequence but focus largely on additional parameters that conform to the superblock configuration.

The maximum volume criterion in sequence 03 is carried forth in this sequence with modifications in the domain and number of the gene pool. The gene pool of scale, offset and extrude are the modifiers assigned to achieve differentiation in volume.

Scale: Acts on each cellular unit of the superblock within a redefined domain of 0.8-1.5. The gene count adds up to 912 genes with 114 genes acting on a singular block from the superblock of 8 blocks.

Offset: Acts on the primary vertical and horizontal axis of the superblock within a domain of 6-10 that determines the width of the axis. A total of 4 genes act independently on XX, y1, y2 and y3 respectively.

Extrude: This is a replacement to the array gene pool of sequence 03 that basically modifies the verticality of the block. In sequence 03, each cellular unit was treated separately in terms of volume, whereas in sequence 04 each subdivision is treated as a singular vertical extrusion.

This reduced the number of faces of the cells to a great extent and reduced the pressure on computational simulation. The domain set is to 15-45 (meters) that defines the height of the cell in each block. There are a total of 912 genes in this gene pool as each cell is extruded separately.

The second criterion of maximum ground exposure is now redefined in terms of urban pockets. The intervention of urban pockets mainly inclines towards creating inner courts but attempting to maintain equal volume simultaneously. There are two distinct domains set for this gene pool, [1] a domain of -10 to +15 that controls the dimensional footprint of the pocket [2] the location of the pocket along the axial length; this is within a remapped domain of 0.0 to 1.0 and is randomized 100%.

This gene pool consists of 5 genes that act on 5 urban pockets; the strategy being: two pockets on “XX” and one pocket each on y1, y2 and y3.

Criterion 01 Maximum Volume

Fig 44.1

fittest Individual

Fig 44.2

Least fit Individual

Fig 44.4

Least fit Individual

Fig 44.6

Least fit Individual

Criterion 02 Maximum Ground Exposure

The third criterion of minimum building exposure to a fixed solar vector has all the four gene pools of “scale”, “offset”, “extrude” and “urban pocket” acting on it simultaneously. The building exposure is determined by the exposure of the faces to the vector and is subject to change in relation to all four gene pools.

The fittest phenotype for the first criterion would be the one with: Maximum domain of scale in all cells, Minimum domain of offset to all axes, Maximum value for vertical extrusions to all cells and vice-versa for the second criterion of maximum ground exposure with minimum dimension of urban pockets. The third criterion of building exposure is subjective in this case as it depends on various factors like location of the urban pockets and position of the solar vector. (Fig 44.5,44.6)

Fig 44.3

fittest Individual

Criterion 03 Minimum Building Exposure

Fig 44.5

fittest Individual

Kaushik Sardesai | Sally Al-Badry | Sharon Ann Philip | Zaqi Fathis

44

4.2 GENERATION 10

Sequence04

The numeric values of genes are randomized to 100% before commencing the simulation. The breeding strategy is developed by pausing the simulation at random generations from 0 to10 and analyzing and comparing the output to its predecessor. Initially all values of mutation, elitism and crossover are 0.1 and gradually adjusted as per the analysis.

ELITISM

It is observed that generation 10 produced almost identical phenotypes in their morphological variation, with fitness values within a specific numerical range, except for the volume, which exhibited a considerable deviation across the evaluated phenotypes.

G10.01

plan

G10.02

view

BUILDING VOLUME : 0.36 GROUND EXPOSURE : 0.91 BUILDING EXPOSURE : 0.76

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plan

G10.03

view

BUILDING VOLUME : 0.6 GROUND EXPOSURE : 0.66 BUILDING EXPOSURE : 0.61

plan

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

MUT. PROBABILITY

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

MUT. RATE

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

CROSSOVER RATE

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

G10.04

view

BUILDING VOLUME : 0.34 GROUND EXPOSURE : 0.87 BUILDING EXPOSURE : 0.67

plan

G10.05

view

BUILDING VOLUME : 0.45 GROUND EXPOSURE : 0.92 BUILDING EXPOSURE : 0.52

plan view

BUILDING VOLUME : 0.48 GROUND EXPOSURE : 0.94 BUILDING EXPOSURE : 0.47

45

4.2 GENERATION 10

G10.06

plan

G10.07

view

BUILDING VOLUME : 0.48 GROUND EXPOSURE : 0.90 BUILDING EXPOSURE : 0.41

plan

G10.08

view

BUILDING VOLUME : 0.25 GROUND EXPOSURE : 0.89 BUILDING EXPOSURE : 0.54

plan

G10.09

plan

view

BUILDING VOLUME : 0.01 GROUND EXPOSURE : 0.83 BUILDING EXPOSURE : 0.22

G10.10

view

BUILDING VOLUME : 0.45 GROUND EXPOSURE : 0.67 BUILDING EXPOSURE : 0.36

plan view

BUILDING VOLUME : 0.38 GROUND EXPOSURE : 0.90 BUILDING EXPOSURE : 0.68

Kaushik Sardesai | Sally Al-Badry | Sharon Ann Philip | Zaqi Fathis

46

Graphs Analysis 4.2.1 GENERATION 10 - ANALYSIS

Normal Distribution

Sequence04

Fitness Value (converted)

DELAUNAY GRAPH Volume Ground Exposure Building Exposure

CONVERGENCE GRAPH

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4.2.1 GENERATION 10 - OBSERVATIONS

The convergence graph of generation 10 shows signs of attaining possible convergence in the future generations, whereas a slight divergence is observed in the criteria of building exposure. The simulation produced more number of phenotypes than the set population, thus resulting in increased comparison. The individuals are ranked according to the combined 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 convergence.

Kaushik Sardesai | Sally Al-Badry | Sharon Ann Philip | Zaqi Fathis

48

4.3 GENERATION 30

Sequence04

The breeding strategy is revised prior to this simulation. The simulation is run from generation 11 to generation 30, which produced over 200 individuals. The crossover rate, elitism and mutation rate is increased to 0.5 while the value of mutation probability is retained to its previous simulation.

ELITISM

It is observed that there is a slight increase in the morphological variation of the phenotypes as compared to the previous simulation that produced identical individuals. The fitness values of volume and building exposure express greater variation, whereas ground exposure values are within a limited range.

G30.01

plan

G30.02

view

BUILDING VOLUME : 0.70 GROUND EXPOSURE : 0.76 BUILDING EXPOSURE : 0.00

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plan

G30.03

view

BUILDING VOLUME : 0.30 GROUND EXPOSURE : 0.60 BUILDING EXPOSURE : 0.70

plan

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

MUT. PROBABILITY

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

MUT. RATE

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

CROSSOVER RATE

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

G30.04

view

BUILDING VOLUME : 1.00 GROUND EXPOSURE : 0.72 BUILDING EXPOSURE : 0.25

plan

G30.05

view

BUILDING VOLUME : 0.52 GROUND EXPOSURE : 0.94 BUILDING EXPOSURE : 0.25

plan view

BUILDING VOLUME : 0.16 GROUND EXPOSURE : 0.51 BUILDING EXPOSURE : 1.00

49

4.3 GENERATION 30

G30.06

plan

G30.07

view

BUILDING VOLUME : 0.05 GROUND EXPOSURE : 0.90 BUILDING EXPOSURE : 0.26

plan

G30.08

view

BUILDING VOLUME : 0.47 GROUND EXPOSURE : 0.90 BUILDING EXPOSURE : 0.26

plan

G30.09

plan

view

BUILDING VOLUME : 0.72 GROUND EXPOSURE : 0.71 BUILDING EXPOSURE : 0.27

G30.10

view

BUILDING VOLUME : 0.27 GROUND EXPOSURE : 0.89 BUILDING EXPOSURE : 0.35

plan view

BUILDING VOLUME : 0.00 GROUND EXPOSURE : 0.60 BUILDING EXPOSURE : 0.34

Kaushik Sardesai | Sally Al-Badry | Sharon Ann Philip | Zaqi Fathis

50

4.3.1 GENERATION 30 - ANALYSIS

Normal Distribution

Sequence04

Fitness Value (converted)

DELAUNAY GRAPH Volume Ground Exposure Building Exposure

CONVERGENCE GRAPH

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4.3.1 GENERATION 30 - ANALYSIS

There is negligible change in the standard deviation factor but increase in the mean fitness over 30 generations. The fact that most individuals are in close proximity to each other in the Delaunay graph and the mesh is highly scattered represents variation in the phenotypes but in pairs or groups only. This is due to limiting the value of mutation probability across the generations.

Kaushik Sardesai | Sally Al-Badry | Sharon Ann Philip | Zaqi Fathis

52

4.5 GENERATION 50

Sequence04

Generation 50 is the outcome of a breeding strategy developed from evaluating three generations; 35, 40 and 45 respectively. The values of mutation probability, rate, crossover and elitism were set in generation 35 as [0.25|0.5|0.5|0.5], generation 40 as [0.75|0.25|0.25|0.5], generation 45 as [0.5|0.75|0.75|0.25] to produce 10 individuals per generation.

Since the strategy changes every five generations, it is difficult to conclude which individuals survive to reproduce and which are killed in the process. The initial attempt was to integrate the simulation of 20 consequent generations into 4x5 and devise the strategy accordingly.

G50.01

plan

G50.02

view

BUILDING VOLUME : 0.33 GROUND EXPOSURE : 0.32 BUILDING EXPOSURE : 0.49t

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plan

G50.03

view

BUILDING VOLUME : 0.24 GROUND EXPOSURE : 0.29 BUILDING EXPOSURE : 0.20

plan

ELITISM

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

MUT. PROBABILITY

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

MUT. RATE

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

CROSSOVER RATE

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

G50.04

view

BUILDING VOLUME : 0.50 GROUND EXPOSURE : 0.55 BUILDING EXPOSURE : 0.54

plan

G50.05

view

BUILDING VOLUME : 0.68 GROUND EXPOSURE : 0.29 BUILDING EXPOSURE : 0.75

plan view

BUILDING VOLUME : 0.31 GROUND EXPOSURE : 0.39 BUILDING EXPOSURE : 0.31

53

4.4 GENERATION 50

G50.06

plan

G50.07

view

BUILDING VOLUME : 0.84 GROUND EXPOSURE : 0.43 BUILDING EXPOSURE : 0.64

plan

G50.08

view

BUILDING VOLUME : 0.21 GROUND EXPOSURE : 0.67 BUILDING EXPOSURE : 0.63

plan

G50.09

plan

view

BUILDING VOLUME : 0.74 GROUND EXPOSURE : 0.77 BUILDING EXPOSURE : 1.00

G50.10

view

BUILDING VOLUME : 0.91 GROUND EXPOSURE : 0.67 BUILDING EXPOSURE : 0.53

plan view

BUILDING VOLUME : 0.73 GROUND EXPOSURE : 0.34 BUILDING EXPOSURE : 0.55

Kaushik Sardesai | Sally Al-Badry | Sharon Ann Philip | Zaqi Fathis

54

4.4.1 GENERATION 50 - ANALYSIS

Normal Distribution

Sequence04

Fitness Value (converted)

DELAUNAY GRAPH Volume Ground Exposure Building Exposure

CONVERGENCE GRAPH

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4.4.1 GENERATION 50 - ANALYSIS

There is negligible change in the mean fitness value but increase in the overall standard deviation. The decrease in mutation probability and increase in the mutation rate resulted in increased morphological variation and standard deviation levels. The reason for negligible change in fitness is due to equivalent values set to mutation rate and elitism, which focus on breeding only the fittest from the population but with increased randomization, which produces incomparable results.

Kaushik Sardesai | Sally Al-Badry | Sharon Ann Philip | Zaqi Fathis

56

4.5 GENERATION 70

Sequence04

As part of the experiment, the breeding strategy of generation 70 is exactly identical to generation 50. Since the breeding strategy was developed three times over successive generations, this simulation progressed with no change in the breeding strategy.

There is significant differentiation observed in all the fitness criteria, which basically deduces the fact that there is a probable increase in variation and fitness. The radar graphs clearly indicate the length of the range that the simulation produced in every generation.

G70.01

plan

G70.02

view

BUILDING VOLUME : 0.37 GROUND EXPOSURE : 0.40 BUILDING EXPOSURE : 0.00

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plan

G70.03

view

BUILDING VOLUME : 0.85 GROUND EXPOSURE : 0.45 BUILDING EXPOSURE : 0.19

plan

ELITISM

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

MUT. PROBABILITY

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

MUT. RATE

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

CROSSOVER RATE

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

G70.04

view

BUILDING VOLUME : 0.00 GROUND EXPOSURE : 0.36 BUILDING EXPOSURE : 0.40

plan

G70.05

view

BUILDING VOLUME : 0.36 GROUND EXPOSURE : 0.62 BUILDING EXPOSURE : 0.47

plan view

BUILDING VOLUME : 0.57 GROUND EXPOSURE : 0.61 BUILDING EXPOSURE : 0.90

57

4.5 GENERATION 70

G70.01

plan

G70.02

view

BUILDING VOLUME : 0.27 GROUND EXPOSURE : 0.56 BUILDING EXPOSURE : 0.65

plan

G70.03

view

BUILDING VOLUME : 1.00 GROUND EXPOSURE : 1.00 BUILDING EXPOSURE : 0.66

plan

G70.04

plan

view

BUILDING VOLUME : 0.62 GROUND EXPOSURE : 0.57 BUILDING EXPOSURE : 0.21

G70.05

view

BUILDING VOLUME : 0.38 GROUND EXPOSURE : 0.18 BUILDING EXPOSURE : 0.64

plan view

BUILDING VOLUME : 0.27 GROUND EXPOSURE : 0.57 BUILDING EXPOSURE : 0.42

Kaushik Sardesai | Sally Al-Badry | Sharon Ann Philip | Zaqi Fathis

58

4.5.1 GENERATION 70 - ANALYSIS

Normal Distribution

Sequence04

Fitness Value (converted)

DELAUNAY GRAPH Volume Ground Exposure Building Exposure

CONVERGENCE GRAPH

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4.5.1 GENERATION 70 - ANALYSIS

It is evident from the transition from generation 50 to 70,which the simulation needs to be run consequently with a clear breeding strategy that can achieve optimum fitness in multiple fitness criteria. The Delaunay graph clearly signifies the phenotypic variation in the population and exhibits signs of achieving optimum fitness and convergence. It is very important to understand the concept of critical threshold in this condition.

“Critical threshold” can be defined in simple terms as “the point of change”. A classic example of critical threshold would be the instance where a single grain of wheat, when dropped on a heap can change the entire geometry of the heap, (it will lose its structure instantly).

Kaushik Sardesai | Sally Al-Badry | Sharon Ann Philip | Zaqi Fathis

60

4.6 GENERATION 90

Sequence04

The breeding strategy is revised based on theoretical hypothesis and tested in this simulation to achieve convergence and optimum fitness. Thus, the mutation probability, rate and elitism are reduced to 0.25, 0.5 and 0.5 respectively. The crossover rate is 0.5 across the entire sequence except for the initial ten generations. No interim generations are evaluated or analyzed in this simulation.

It is observed that nearly all the phenotypes produced in generation 90 are exactly identical with only the gene pool of extrude and scale enhancing the variation in the individuals. The urban pocket gene pool is almost extinct across the last 20 generations.

G90.01

plan

G90.02

view

BUILDING VOLUME : 0.17 GROUND EXPOSURE : 0.64 BUILDING EXPOSURE : 0.10

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plan

G90.03

view

BUILDING VOLUME : 0.32 GROUND EXPOSURE : 0.16 BUILDING EXPOSURE : 0.64

plan

ELITISM

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

MUT. PROBABILITY

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

MUT. RATE

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

CROSSOVER RATE

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

G90.04

view

BUILDING VOLUME : 0.09 GROUND EXPOSURE : 0.37 BUILDING EXPOSURE : 0.10

plan

G90.05

view

BUILDING VOLUME : 0.11 GROUND EXPOSURE : 0.39 BUILDING EXPOSURE : 0.15

plan view

BUILDING VOLUME : 0.08 GROUND EXPOSURE : 0.37 BUILDING EXPOSURE : 0.04

61

4.6 GENERATION 90

G90.06

plan

G90.07

view

BUILDING VOLUME : 0.09 GROUND EXPOSURE : 0.38 BUILDING EXPOSURE : 0.02

plan

G90.08

view

BUILDING VOLUME : 1.00 GROUND EXPOSURE : 0.38 BUILDING EXPOSURE : 0.02

plan

G90.09

plan

view

BUILDING VOLUME : 0.30 GROUND EXPOSURE : 1.00 BUILDING EXPOSURE : 0.02

G90.10

view

BUILDING VOLUME : 0.26 GROUND EXPOSURE : 0.39 BUILDING EXPOSURE : 0.72

plan view

BUILDING VOLUME : 0.28 GROUND EXPOSURE : 0.43 BUILDING EXPOSURE : 0.42

Kaushik Sardesai | Sally Al-Badry | Sharon Ann Philip | Zaqi Fathis

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4.6.1 GENERATION 90 - ANALYSIS

Normal Distribution

Sequence04

Fitness Value (converted)

DELAUNAY GRAPH Volume Ground Exposure Building Exposure

CONVERGENCE GRAPH

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4.6.1 GENERATION 90 - ANALYSIS

The radar charts indicate that each of the phenotype has reached its optimum fitness value and chances of further evolution or development is highly unlikely.

The mean fitness value and standard deviation factor both indicate an incremental increase in fitness and variation. The Delaunay mesh is not scattered across the three axes of the graph, which results in a limited number of fit phenotypes. The convergence graph highlights the probability that the simulation has reached the pareto optimal and absolute convergence.

Kaushik Sardesai | Sally Al-Badry | Sharon Ann Philip | Zaqi Fathis

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4.7 PHENOTYPE COMPARISON

Sequence04

GROUND EXPOSURE

OVERALL

G90.05 = 0.45

G70.07 = 2.66

G30.05 = 0.16

G70.09= 1.0

FITTEST INDIVIDUAL

LEAST FIT INDIVIDUAL

FITTEST INDIVIDUAL

LEAST FIT INDIVIDUAL

BUILDING VOLUME

BUILDING EXPOSURE

G30.10= 0

G10.06 = 1.0

G30.01 = 0.00

G30.05 = 1.00

FITTEST INDIVIDUAL

LEAST FIT INDIVIDUAL

FITTEST INDIVIDUAL

LEAST FIT INDIVIDUAL

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4.8 OBSERVATIONS

Normal Distribution

GENERATION 10

GENERATION 30

GENERATION 50

GENERATION 70

GENERATION 90

Average Fitness Value

The graph shows the comparison of the five evaluated generations in this sequence. It is observed that generation 10 and 30, 50 and 70 have negligible change in their respective variation but a steady increase in the average fitness value. The main transition of development is seen from generation 10 to 50 to 90 or 30 to 70 to 90. It is thus possible to conclude from the experiments of this sequence that the values of mutation probability should be within the domain of 0.25 to 0.50, mutation rate within 0.5 to 0.75, crossover rate 0.5 and elitism within the domain of 0.5 to 0.75 respectively.

As a part of further study and exploration, it would be interesting to experiment by modifying the crossover rate, as it remained constant throughout the sequence. To induce greater morphological variation, it would be logical to redefine the domains of the gene pool to ensure their effectiveness throughout the sequence and avoid early extinction or dormancy.

Kaushik Sardesai | Sally Al-Badry | Sharon Ann Philip | Zaqi Fathis

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4.9 SUPER BLOCK COMPARISON

45 m

Block:

1-G0.1

Block:

2-G30.10

Block:

2-G50.01

Block:

3-G70/05

Block:

4-G90.10

Block Length: Block Width: Street Width:

423 m 247 m 10 m [maximum] 5 m [minimum] 45 m

Block Length: Block Width: Street Width:

412 m 241m 10 m [maximum] 5 m [minimum] 45 m

Block Length: Block Width: Street Width:

413 m 242 m 10 m [maximum] 5 m [minimum] 45 m

Block Length: Block Width: Street Width:

413 m 242 m 10 m [maximum] 5 m [minimum] 45 m

Block Length: Block Width: Street Width:

411 m 241 m 10 m [maximum] 5 m [minimum]

Building Depth:

Plot coverage:

65.46%

FAR:

1.96

Height [storey]:

5 [minimum]

Building Depth:

Plot coverage:

61%

FAR: Height [storey]:

5 [minimum]

Sun Exposure:

% 16.9 Building Surface

% 81.63 Courtyard Surface

% 83.1Courtyard Surface

Building Exposure: Ground Exposure:

1 0 1

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Height [storey]:

5 [minimum]

15 [maximum]

% 18.37 Building Surface

Volume:

Plot coverage: FAR:

15 [maximum]

Sun Exposure:

Building Depth:

Volume: Sun Exposure: Ground Exposure:

0.92 0.8 0.7

Building Depth:

Plot coverage:

62%

FAR:

900

Height [storey]:

5 [minimum]

15 [maximum]

Sun Exposure:

% 13. 95 Building Surface

Sun Exposure: Ground Exposure:

0.7 0.17 0.09

Plot coverage: FAR: Height [storey]:

15 [maximum]

Sun Exposure:

% 86.04 Courtyard Surface

Volume:

Building Depth:

% 15.48 Building Surface

Sun Exposure: Ground Exposure:

0.28 0.63 0.3

900 5 [minimum] 15 [maximum]

Sun Exposure:

% 14.84 Building Surface % 85.15 Courtyard Surface

% 84.51Courtyard Surface

Volume:

64%

Volume: Sun Exposure: Ground Exposure:

0.29 0.7 0.6

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5.0 SUMMARY

FITNESS CRITERIA

GROWTH STRATEGY

GENE POOL

01.

maximum volume

Mx

Mz

My

Sx

Sy

Sz

Sxyz

1st fittest

SEQUENCE 02

01.

volume

Rz

Ax

Ay

Az

Axyz

GEN. 04 :

Array (A)

Rotate (R)

surface area

3 2

02.

BODY PLAN

Cx

height

Cy

fittest GEN. 05 : GEN. 06 :

Cz

random combination of six genes 01

05

04

06

Copy (C)

width

01.

SEQUENCE 03 SEQUENCE 04

03

02

01

maximum volume 0.8

02.

BLOCK

least fit

fittest

Ry

Rx

4

2nd fittest

GEN. 03 :

PRIMITIVE

1

GEN. 02 :

Scale (S)

Move (M)

random combination of six genes

03.

01.

1.0

Scale (S)

maximum ground exposure

minimum building exposure

0.0

1.0

10 Floors

16 Floors

elitism mut. probability mut. rate crossover rate

GEN. 03 GEN. 04 GEN. 05

elitism mut. probability mut. rate crossover rate

GEN. 06 GEN. 07 GEN. 08 GEN. 09

elitism mut. probability mut. rate crossover rate

maximum volume 1.2

Open space size

Scale (S)

maximum ground exposure

minimum building exposure

1.0

5

External road size (E)

GEN. 20 GEN. 30 GEN. 40 GEN. 50 GEN. 60 GEN. 70

10

Extrude in z direction

03

GEN. 00 GEN. 01 GEN. 02

GEN. 00 GEN. 10

SUPERBLOCK

03.

Array in z direction

2.0

Offset (O)

0.8

02.

1.2

02

Open space location GEN. 80 GEN. 90

elitism mut. probability mut. rate crossover rate elitism mut. probability mut. rate crossover rate elitism mut. probability mut. rate crossover rate elitism mut. probability mut. rate crossover rate elitism mut. probability mut. rate crossover rate

06

05

04

09

08

07

10

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Normal Distribution

SEQUENCE 01

GEN. 01 :

GRAPH COMPARISON

Kaushik Sardesai | Sally Al-Badry | Sharon Ann Philip | Zaqi Fathis

Average Fitness Value

68

6.0 CONCLUSION This main purpose of this paper was to implement the concepts of evolutionary development in computation and devise emergent design strategies translated from the theories of embryological development in a hierarchical sequence. The concepts and techniques of this advancing research offer tremendous scope and potential in the architectural realm today.

The shift from manually operating a singular primitive to simulation based evolutionary algorithms in evolving an urban superblock has bolstered the logic and importance of Evo-Devo and its extensive vocabulary, in urban design and other branches of architecture. However, the concept of Hox genes and the intricate relationship of different segments in the body plan requires an in depth study and understanding as it is crucial at any stage of evolution, be it biology or architecture.

The current research uses the basic modifier operations from the Rhinoceros syntax library that advances to Grasshopper and eventually Octopus. This marks the advancement of computational tools, but the gene operations remain the same. The modeling language of Rhinoceros and Grasshopper with the Octopus plugin is “surfaces” and “meshes” that increases the computational load and limits the number of simulations.

Thus, if all the genes are scripted from the first sequence, it promotes the concept of combining two or more genes to produce one new gene. These permutations can contribute to regenerating unique genome sequences as per the growth strategy and fitness criteria.

The theories of evolutionary development, though explicit, need to be systematically integrated in genetic algorithms and computation to harness it to the fullest strength and capacity of data structure and management, which is deficit to a certain extent.

As a conclusion, the four sequences attempt all possible strategies and experiments to interpolate new avenues of explorations but demand a more systematic approach to deduce quantifiable and appropriate criteria with more environmental relevance. It would be interesting to figure out if evaluating a higher population within one generation is feasible over evaluating higher number of generations with a limited population count. This co-relation can be resolved to form a standard factor or ratio between the numbers of generations to its population size.

Another interesting possibility of innovating and expanding this research would be to redefine and script the genes in Python or C++, which would reduce the computational load to a great extent.

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7.0 REFERENCES

BOOKS 1. Carroll, S. (2005). Endless forms most beautiful. New York: Norton.

2. Weinstock, M. (2010). The architecture of emergence. Chichester, U.K.: Wiley.

3. Weinstock, M., Hensel, M. and Menges, A. (2010). Emergent technologies and design : towards a biological paradigm for architecture. Oxon: Routledge 4. Thompson, D. (1961) On growth and form. Cambridge: Cambridge University Press 5. Sakamoto, T. and FerrĂŠ, A. (2008). From control to design. Barcelona: Actar-D. 6. Burry, M. (2011). Scripting cultures. Chichester, UK: Wiley.

ARTICLES 7. Makki, M., Navarro, D. and Farzaneh, A. (2015). The Evolutionary Adaptation of Urban Tissues through Computational Analysis. Education and Research in Computer Aided Architectural Design in Europe, 2, pp.563-571.

8. Makki, M. (2015). An Evolutionary Model for Urban Development. International Seminar on Urban Form: New Visions for Urban Life.

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COURSE DIRECTOR: Michael Weinstock George Jeronimidis STUDIO MASTER: Evan Greenberg MASTER TUTOR: Mohammed Makki