Emergence by Tejas Sidnal

EMERGENCE

Viral Doshi Yan Bai Tejas Sidnal Jiangyue Xia

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

Architectural Association School of Architecture

CONTENTS SECTION

PAGE

Introduction

Sequence 1

8 -38

Genome Structure

Population 1

10-13

Population 2

14-17

Population 3

18-21

Population 4

22-27

Population 4A

28-33

Population 5

28-38

Sequence 2

39 -58

Body Plan

Strategies

43-45

Population 6

46-47

Population 6A

48-49

Population 7

50-52

Population 8

53-56

Conclusion

57-58

Sequence 3

59-82

Fitness Criteria

59-64

Strategies

65-66

Population 9

Population 9A

Population 10

69-70

Galapagos Experiments

71-81

Conclusion

Biblography

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

Architectural Association School of Architecture

INTRODUCTION This document consists of the work done during two weeks of emergence workshop at the Emergent Technologies and Design at the Architectural Association School of Architecture during January 2013. The aim of this workshop was to understand evolutionary design principles and try and test evolutionary algorithms based on various fitness criterias and evaluating them repeatedy by changing the fitness criterias based on their results.The project consisted of 3 sequences. The aim of the project was to understand the relationship and the effect of the fitness criteria in evaluation of an individual over generations. The idea was to evaluate and analyse different individuals by various parameters and computational testing. Sequence 1 In sequence one, four sided pyramid was chosen as the primitive. The "gene" was defined with specific instruction . These instructions became the various "genes " of the individual . Transformations were made on the individual selecting specific genes randomly. These set of Instructions were the " genome " of the individual. After randomly selecting the genes first population was generated. First population was evaluated on a certain fitness criteria i.e (Height / Total Surface Area ) and ranked accordingly. The second population was created by again randomly selecting few indivuduals and modifying the first population.The third population was created by selecting few individuals from the previous two populations and cross breeding them. From the fourth population a specific breeding, killing and mutation strategy was followed. Two average, two fit and two monsters were selected from the population and bred to create the next population. Rest of the Individuals were killed in the previous population. Mutation was applied over 40 % of the individuals to create the next population. The last population in this sequence was created with the same strategy . Sequence 2 In sequence two , body plan was redefined by splitting it into two halves and named them as "Part a" and "Part b" to have differential growth and created a new gene structure as well. In this sequence an environmental factor had to be considered for which our growth pattern was defined accordingly. We were interested in looking at maximising the south surface for part b and developing the part a as a support system. Consistently carry forwarding the same breeding, killing and mutation strategies as in sequence one the next two populations were created. Looking at hierarchical assembly for the last population in this sequence ,individuals with nested genes were bred and evaluated on the same fitness criteria. Results of the populations were compared and looked at the effect of the environmental factor on the individual.

modified according to the inputs received from Galapagos (scripted process). More fitness criterias were added so that Part A and Part B can specialise its genes for its specific purpose. Fitness criterias were given weightage and individuals were evaluated and ranked accordingly. Parallel to these we ran various scripts on Galapagos and tried to understand its potential and possibilities and the relation of the growth of part a with respect to part b. Evo-Devo Evolution is the change in inherited characteristics over several generations and Embryological development is the growth of an adult from an embryo. Embryological development takes place in one generation however evolution is compared over several generations. Both consists of two different time scales. However " Evo-Devo " has given us an insight of the relation between the Embryological development and the Evolution. It even explores the process as to how organisms in nature have specific breeding and mutation strategy and how it affects the development of the organism. The book also talks about how form is related to development and how a single cell develops into a complex form and through several successive generations specialises its certain parts of the body for specific functions. It explains the body plan and how the embryo develops from a single cell after dividing it into 4 parts and homeobox which acts as a switch turning on and off certain genes at specific regions. The homeobox allows to create variations in the individual as it controls the genes and allows various organisms to grow and adapt to their environmental pressure subjected. This paper explores the theory of "Evo-Devo" and how it could be incorporated to develop architectural forms responding to various environmental pressures and specialised for certain functions using evolutionary algorithms. It also exposes us to the potentials and possibilities of evolutionary algorithm.

Sequence 3 With the body plan considered in sequence two various natural systems were looked upon for inspiration and were particularly interested in sunflower as it suited best for our hypothesis. This sequence was divided into two parts i.e. manual process and scripted which ran parallel. Fitness criteria's were 6

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

Architectural Association School of Architecture

1 ION T A UL POP

2 ION T A UL POP

3 ON I T ULA P PO

4 ON I T ULA P PO

P O P U LA TI O N

A N4 O I LAT U P PO

SEQUENCE 1

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

Sequence 1

Definition of Gene A

Copy

C(0,0,0)

B01 B02 B03 B04

M(xY)(a/4) M(Xy)(-a/4) M(xY)(a/2) M(Xy)(a/2)

Move

Mirror

Mi(xz)(P1,P3)

Polar Array

D01 D02 D03 D04

PA(xz)(P4,3,90) PA(xy) [(a/4,0,0),4,180] PA(xy)(P1,5,360) PA(xy)(P5,5,360)

Scale 1d

E01 E02 E03 E04 E05 E06

S1(Xy)(P0,0.5) S1(Xz)(P3,1.5) S1(xZ)(P0,4) S1(Xz)(P5,0.75) S1(Xy)(P3,2) S1(xY)(P3,0.6)

F01 F02 F03 F04

S2(xz)(P3,1.5) S2(xy)(P3,2) S2(xy)(P4,0.4) S2(xz)(P5,0.8)

G01 G02 G03 G04

R(Xz)(P3,15) R(Xz)(P3,45) R(xZ)(P3,45) R(Xz)(P4,270)

a a/2

Plane XY

P1 P3

Primitive : Pyramid

"Pyramid" was selected as the primitive for generating the first population for sequence 1. The set of instructions were given to define the various genes for the individual. Five vertices and the center of the square base of the pyramid were used to define the set of instructions . Any transformation made to the pyramid was related to the last modified phenome. Using these genes from the gene structure randomly the first population was created .

E 2a P3

X Plane XY

Gene Structure Seven families with sets of instructions were created under which each instructions (gene) were defined. A total of 26 genes were defined for the gene structure. Random selection of these instruction in random order resulted in the formation of population 1 which was evaluated with a fitness criteria.

X Y

G Z

45째 P3

Architectural Association School of Architecture

Scale 2d

Rotate

SEQUENCE 1

Primitive

P01.15 G01.15

E02 G01

P01.14 G01.14

D03

P01.13 G01.13

D02

P01.12 G01.12

G04

P01.11 G01.11

E01

P01.10 G01.10

G02

P01.09 G01.09

G03 D01

P01.08 G01.08

E03

P01.07 G01.07

F02

P01.06 G01.06

B04

P01.05 G01.05

B03 B02

P01.04 G01.04

E02

P01.03 G01.03

D04

P01.02 G01.02

F01

P01.01 G01.01

B03

B01

POPULATION 1

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

SEQUENCE1- Population 1

P01.01 G01.01

P01.02 G01.02

P01.03 G01.03

P01.04 G01.04

P01.09 G01.09

P01.10 G01.10

P01.11 G01.11

P01.12 G01.12

Architectural Association School of Architecture

P01.05 G01.05

P01.06 G01.06

P01.13 G01.13

P01.14 G01.14

P01.07 G01.07

P01.08 G01.08

P01.15 G01.15

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

SEQUENCE1- Population 1 Fitness Criteria: Height/ Total Surface Area Organisms in the natural world survive over the limited resource available. They try to adapt themselves to the environmental pressures imposed upon them and thereby specialising their parts for those specific functions. Looking into the similar theory of "survival of the fittest" ,a fitness criteria is considered to evaluate the individual and rank them over their parameters. A specific killing strategy is taken into account and certain unfit populations are killed depending upon the fitness criteria. In nature ,organisms keep evolving over generations as the environmental pressure keep changing. Modifying the fitness criteria over successive generations make the individuals in the population evolve and specialise for specific functions. Population 01

Height(H)

Total Surface Area(TSA)

H/TSA

Rank

Normal Distribution

1.07

ʼƔʷʷʷʷ

ʺʿʿƔʿʻʿʽ

ʷƔʷʸʹˀ

ʸʺ

ʼƔʾʼʿʺ

1.14

ʼƔʷʷʷʷ

ʺʻʷƔʻʻʷʽ

ʷƔʷʸʻʾ

ʸʹ

ʽƔʻʺʼʼ

1.13

ʼƔʷʷʷʷ

ʸʾʷƔʾʹˀʾ

ʷƔʷʹˀʺ

ʸʸ

ʸʹƔʼˀʸʾ

1.04

ʼƔʷʷʷʷ

ʸʽʷƔʹʸʷʿ

ʷƔʷʺʸʹ

ʸʷ

ʸʺƔʺʽʽʻ

1.05

ʼƔʷʷʷʷ

ʸʻʽƔʽʺʷʷ

ʷƔʷʺʻʸ

ʸʻƔʻʺˀʸ

1.12

ʾƔʷʾʷʷ

ʹʷʹƔʹʼʻʹ

ʷƔʷʺʼʷ

ʸʻƔʾʺʷʻ

1.06

ʼƔʷʷʷʷ

ʸʻʸƔʼʾʿʷ

ʷƔʷʺʼʺ

ʸʻƔʿʻʿʿ

1.03

ʼƔʷʷʷʷ

ʸʸʷƔʽʸʼˀ

ʷƔʷʻʼʹ

ʸʽƔʿʾˀʽ

1.02

ʾƔʼʷʷʷ

ʸʻʷƔʸˀʿʷ

ʷƔʷʼʺʼ

ʸʽƔʻʷʺʷ

1.15

ʽƔʹʷʷʷ

ʸʸʸƔʻʼʸʷ

ʷƔʷʼʼʽ

ʸʼƔˀʼʿʽ

1.09

ʿƔʻˀʷʷ

ʸʻʻƔʹˀʺʿ

ʷƔʷʼʿʿ

ʸʼƔʷʾʾʾ

1.01

ʼƔʷʷʷʷ

ʿʷƔˀʷʸʾ

ʷƔʷʽʸʿ

ʸʻƔʷʾʹʺ

1.10

ʾƔʷʾʷʷ

ʸʸʸƔʿʷʺʻ

ʷƔʷʽʺʹ

ʸʺƔʼʺʺʽ

1.08

ʹʷƔʷʷʷʷ

ʹʹʽƔʼʼʽʻ

ʷƔʷʿʿʺ

ʸʻ

ʺƔʾʼʿʾ

1.11

ʼƔʷʷʷʷ

ʼʺƔˀʼʾʿ

ʷƔʷˀʹʾ

ʸʼ

ʹƔʽʾʹʽ

Mean

0.0474

Standard Deviation

ʷƔʷʹʺʼ

The first population was ranked and evaluated on the fitness criteria ( Height / Total Surface Area ). The individuals close to the mean of the bell curve were considered as most fit and the individuals furthest away from the mean of the bell curve were considered as monsters. In the first population it was observed that the individuals we evenly distributed along the bell curve.

Architectural Association School of Architecture

Normal Distribution 18

0.00

0.02

0.04

0.06

0.08

0.10

Distribution of Values 0.10

0.08

0.06

0.04

Mean

Standard deviation

Population 1 :

0.0474

0.0235

Population 2 :

0.0489

0.0175

0.02

SEQUENCE1- Population 2

Åº¿Ű»º

P01.15 G01.15

E02 G01

P01.14 G01.14

D03

P02.13 G02.13

D02 F04 E06

P02.12 G02.12

G04

P01.11 G01.11

E01

P01.10 G01.10

G02

P01.09 G01.09

G03 D01

P02.08 G02.08

E03 E05

P02.07 G02.07

F02

P01.06 G01.06

B04

P02.05 G02.05

B03 B02 E04

P01.04 G01.04

E02

P01.03 G01.03

D04

P01.02 G01.02

F01

B03

B03 E04

B01 F03

B01

POPULATION 2

P01.01 G01.01 Population 1

Population 2

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

SEQUENCE1- Population 2

P01.01 G01.01

P01.02 G01.02

P01.09 G01.09

P01.10 G01.10

Architectural Association School of Architecture

P01.03 G01.03

P01.11 G01.11

P01.04 G01.04

P02.12 G02.12

P02.05 G02.05

P01.06 G01.06

P02.07 G02.07

P02.13 G02.13

P01.14 G01.14

P01.15 G01.15

P02.08 G02.08

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

SEQUENCE1- Population 2

Normal Distribution Population 02

Height(H)

Total Surface Area(TSA)

H/TSA

Rank

Normal Distribution

1.14

ʼƔʷʷ

ʺʻʷƔʻʻʷʽ

ʷƔʷʸʻʾ

ʸʻ

ʺƔʺʽʸʷ

1.04

ʼƔʷʷ

ʸʽʷƔʹʸʷʿ

ʷƔʷʺʸʹ

ʸʺ

ʸʺƔʽʿʿˀ

1.06

ʼƔʷʷ

ʸʻʸƔʼʾʿʷ

ʷƔʷʺʼʺ

ʸʹ

ʸʽƔʿˀʹʺ

2.12

ʾƔʷʾ

ʸʾʼƔʸʾʷʷ

ʷƔʷʻʷʻ

ʹʷƔʹʾʽʺ

2.05

ʼƔʷʷ

ʸʹʷƔˀˀʷʷ

ʷƔʷʻʸʺ

ʹʷƔʾˀʿʼ

1.09

ʽƔʷʷ

ʸʻʸƔʹʽʷʿ

ʷƔʷʻʹʼ

ʹʸƔʺʼʹʼ

1.03

ʼƔʷʷ

ʸʸʷƔʽʸʼˀ

ʷƔʷʻʼʹ

ʹʹƔʺʺʾʺ

2.13

ʺƔʹʼ

ʽʿƔʸʻʷʷ

ʷƔʷʻʾʾ

ʹʹƔʾʿʽʹ

2.07

ʼƔʷʷ

ˀʼƔʸʽʷʷ

ʷƔʷʼʹʼ

ʹʹƔʺʻʹˀ

1.02

ʾƔʼʷ

ʸʻʷƔʸˀʿʷ

ʷƔʷʼʺʼ

ʹʹƔʷʼʽʹ

2.08

ʹʷƔʷʷ

ʺʾʷƔʸʼʷʷ

ʷƔʷʼʻʷ

ʹʸƔʿʽʾʾ

1.15

ʽƔˀʾ

ʸʹʸƔʿʽʸʼ

ʷƔʷʼʾʹ

ʹʷƔʺˀʺʻ

1.01

ʼƔʷʷ

ʿʷƔˀʷʸʾ

ʷƔʷʽʸʿ

ʸʷ

ʸʾƔʺʾʹʽ

1.10

ʾƔʷʾ

ʸʸʸƔʿʷʺʻ

ʷƔʷʽʺʹ

ʸʸ

ʸʽƔʹˀʼʹ

1.11

ʼƔʷʷ

ʼʺƔˀʼʾʿ

ʷƔʷˀʹʾ

ʸʼ

ʷƔˀʿʾʻ

Mean

0.0489

Standard Deviation

ʷƔʷʸʾʼ

WŽƉƵůĂƚŝŽŶ ϭ

WŽƉƵůĂƚŝŽŶ Ϯ

0.00

0.02

0.04

0.06

0.08

0.10

Distribution of Values 0.10

0.08

0.06

The second population was created by randomly selecting few individuals and modifying the first population. The second population was ranked and evaluated in the same way on the same fitness criteria. The individuals from both the populations were compared to check their individual fitness and the overall fitness of the population. The individual fitness of the population had improved and more individuals had grouped around the mean.

Mean

Standard deviation

Population 1 :

0.0474

0.0235

Population 2 :

0.0489

0.0175

0.04

0.02

Architectural Association School of Architecture

SEQUENCE1- Population 3 P03.15 G03.15

G01

P03.14 G03.14

E04 D02

P03.13 G03.13

D02 E01 F02

P03.12 G03.12

G01

B01

P03.11 G03.11

B01

P03.10 G03.10

E02

P03.09 G03.09

E02 E02 G01

P03.08 G03.08

Population 3

B02 E04 F02

B03 F02 B02

E04 G01 E01

P03.06 G03.06

B03 B02

P03.05 G03.05

E02 F02 G01 A

B01

B01 G01

P03.07 G03.07

P03.04 G03.04

Population 2

B01

B01 E01

P03.03 G03.03

E01 E02

P03.02 G03.02

D02 E04

P03.01 G03.01

E04

B03

POPULATION 3 B01 18

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

SEQUENCE1- Population 3

P03.01 G03.01

P03.09 G03.09

Architectural Association School of Architecture

P03.02 G03.02

P03.10 G03.10

P03.03 G03.03

P03.11 G03.11

P03.04 G03.04

P03.12 G03.12

P03.05 G03.05

P03.06 G03.06

P03.07 G03.07

P03.13 G03.13

P03.14 G03.14

P03.15 G03.15

P03.08 G03.08

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

SEQUENCE1- Population 3

Normal Distribution Population 03

Height(H)

Total Surface Area(TSA)

H/TSA

Rank

Normal Distribution

3.08

ʹʿʾƔʼʾˀʼʹʿ

ʷƔʷʸʾʺʿʽʻˀʼ

ʸʺ

ˀƔʾʷʺʻʿʼʷʸʽ

3.13

ʹʻʾƔʷʼ

ʷƔʷʹʷʹʺʿʿʸʿ

ʸʹ

ʸʸƔʾˀʺʿʸʷʼ

3.05

ʾƔʼʾ

ʺʻʽƔʿʼʷʸˀʾ

ʷƔʷʹʸʿʹʻˀʿʻ

ʸʸ

ʸʹƔˀˀˀʼʷʷʿ

3.15

ʼƔʾ

ʹʷʽƔʺʿ

ʷƔʷʹʾʽʸʿˀʼʼ

ʸʷ

ʸʾƔʺʺʻˀʺʻʾʿ

3.02

ʸʽʷƔʺʽʿʺˀʾ

ʷƔʷʺʸʸʾʿʹʸʺ

ʸˀƔʽʹʻʾʼʺʺʼ

3.11

ʸʼʸƔʽˀ

ʷƔʷʺʹˀʽʸˀʽʹ

ʹʷƔʼʾʷʾʾʿˀʺ

3.10

ʽƔʹ

ʸʿʷƔʺʻ

ʷƔʷʺʻʺʾˀʼʷʼ

ʹʸƔʹʷʹʷʺʻʽʾ

3.14

ʸʹʽƔˀʽ

ʷƔʷʺˀʺʿʹʻʿʺ

ʹʹƔʻʸʿʼʺʽˀʻ

3.03

ʸʸʽƔʿʹʼʷʺʹ

ʷƔʷʻʹʾˀˀʷʻʾ

ʹʹƔʹʼʸʾʸʾˀˀ

3.06

ʸʸʽƔʹˀʽʸˀʺ

ʷƔʷʻʹˀˀʺʽʽˀ

ʹʹƔʹʸʾʼʷʺʷʹ

3.09

ʾƔʷʺ

ʸʼˀƔʿˀ

ʷƔʷʻʺˀʽʾʾʹʿ

ʹʹƔʷʷʾʺʹʿʹʿ

3.12

ʼƔʾ

ʸʸʿƔˀʺ

ʷƔʷʻʾˀʹʾʺʼʹ

ʹʷƔʼʹʿʷʸʻʺʻ

3.04

ˀˀƔʻʼʽʹʽʹ

ʷƔʷʼʷʹʾʺʺʼʼ

ʸˀƔʹʻʹʽʺˀʺʽ

3.07

ʼƔʹ

ʽˀƔʻʷʺʿʻʽ

ʷƔʷʾʻˀʹʺʿʷʹ

ʸʻ

ʺƔʺˀˀʷʽʺʸʽˀ

3.01

ʾƔʼ

ˀʼƔˀʽʺ

ʷƔʷʾʿʸʼʼʸʹʹ

ʸʼ

ʹƔʺʻʾˀʷʼʾʾʽ

Mean

0.040

Standard Deviation

ʷƔʷʸʾʿ

25 WŽƉƵůĂƚŝŽŶ ϭ

WŽƉƵůĂƚŝŽŶ Ϯ WŽƉƵůĂƚŝŽŶ ϯ

15 10 5 0

0.01

0.03

0.05

0.07

0.09

Distribution of Values 0.10

0.08

0.06

The third population was created by randomly selecting few individuals from the previous two populations and cross breeding them. The population was evaluated and ranked over the same fitness criteria. The phenomes of the individual in the population looked very similar to the previous populations showing very less variation. Population looked similar due to the selection of the genes and the gene structure and the fitness of the population was similar to the previous population but the overall fitness (mean value) of the population had reduced.

Mean

Standard deviation

Population 1 :

0.0474

0.0235

Population 2 :

0.0489

0.0175

Population 3 :

0.0400

0.0178

0.04

0.02

Architectural Association School of Architecture

SEQUENCE1- Population 4

Descendents

P03.15 G03.15

G01 A B02 E04 F02

P03.02 G03.02

D02 E04

P04.10 G04.10

E04 A E04 G01 A E01

P04.09 G04.09

B01 A B01 A E01

ŰÊ

P04.08 G04.08

G01 A G01

ŰÊ

P04.07 G04.07

E02 G01 G01

P04.06 G04.06

E01 A B01 E01 D01

P04.05 G04.05

B01 E01 E04 G01

ŰÊ

P04.04 G04.04

F02 B02 E02 G01

ÄŰÊ

P04.03 G04.03

E02 E02 A B03

P04.02 G04.02

B01 A D01 B01

P04.01 G04.01

G01 A E04 E03

ÄŰÊ

Non mutated

ÄŰÊ

ŰÊ

ŰÊ Descendents

ÄŰÊ

Population 3

Population 4

POPULATION 4

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

Architectural Association School of Architecture

SEQUENCE 1 - Population4 Killing strategy

Breeding Strategy

Descendents A1 - Average 1 50 % R

Child 1

1 Pair A2 - Average 2 50 % R

A1 - Average 1

M1 - Monster 50 % R

Child 2

Child 1

1 Pair A2 - Average 2

M2 - Monster

Child 2

50 % R M3 - Monster M3 - Monster

Child 1

30 % R

M2 - Monster 3 Pair

A3 - Average

Child 2

70 % R

A3 - Average 3 M1 - Monster Descendents Descendents

Population 3

Breeding is an important stage in life of an organism. During this stage, genes from individuals are passed on to the next generations. Dominant genes are randomly selected from parents and passed on to the next generation. These carry important information which plays an important role in the development of an individual. During this stage, two average, two fit and two less fit individuals were selected from the population and bred to create the next populations. One pair of average - average individuals, one pair of less fit- less fit individuals and 3 pairs of less fit- average individuals were bred. The parents of average - average and less fit- less fit were bred by randomly selecting 50% of the genes from each genome to create an individual. For bredding of less fit - average individuals we selected 70% genes from the average parent and the rest 30% genes from the less fit parent.

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

SEQUENCE 1 - Population4

P04.01 G04.01

P04.02 G04.02

P04.07 G04.07

P04.08 G04.08

Architectural Association School of Architecture

P04.03 G04.03

P04.09 G04.09

P04.04 G04.04

P04.10 G04.10

P04.05 G04.05

P04.06 G04.06

P03.02 G03.02

P03.15 G03.15

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

SEQUENCE 1 - Population 4

Normal Distribution Population 04

Height(H)

Total Surface Area(TSA)

H/TSA

Rank

Normal Distribution

4.03

ʹʾˀƔʿʸʼʻʸʿ

ʷƔʷʸʾʿʽʿˀʹʹ

ʸʷ

ʽƔʸʻʻʿˀˀʸʸʸ

4.04

ʾƔʽ

ʺʻʽƔʿʼʷʸˀʾ

ʷƔʷʹʸˀʸʸʻʾʾ

ʾƔʺʷʼʽʷʷʾʽʼ

3.15

ʼƔʾ

ʹʷʽƔʺʿ

ʷƔʷʹʾʽʸʿˀʼʼ

ˀƔʷʸʷʼʺʷˀˀʹ

3.02

ʸʽʷƔʺʽʿʺˀʾ

ʷƔʷʺʸʸʾʿʹʸʺ

ʸʷƔʷʽʸʽˀˀʻʸ

4.09

ʸʸʽƔʹˀʽʸˀʺ

ʷƔʷʻʹˀˀʺʽʽˀ

ʸʹƔˀʽʽʷʷʻʾʹ

4.06

ʼƔˀ

ʸʸʻƔʻʸʻʸʼʸ

ʷƔʷʼʸʼʽʾʷʻʿ

ʸʺƔˀʿʺʾʺʸʾʻ

4.08

ʽƔʸ

ʸʷʽƔʾʷˀʺʾʸ

ʷƔʷʼʾʸʽʻʽʸʻ

ʸʺƔˀʿʼʻʽʺʻʾ

4.07

ʽƔˀʿ

ʸʸʸƔʻʼʷˀˀʾ

ʷƔʷʽʹʽʹʿʻʹʸ

ʸʺƔʻʾʹʻʸʺʼʺ

4.02

ʿƔʼ

ʸʹʾƔʿʸʼʹʼʸ

ʷƔʷʽʽʼʷʹʹʺʽ

ʸʹƔʿʹʿˀʼʼ

4.10

ʼƔʺʼ

ʾʿƔʻʾˀʾˀʾ

ʷƔʷʽʿʸʾʷʻʸʸ

ʸʹƔʻʿˀʼʾʽʽʸ

4.01

ʹʷƔʹ

ʹʷʽƔʹˀʻʺʹʺ

ʷƔʷˀʾˀʸʿʺʼʸ

ʸʸ

ʻƔʺʺʽʸʾʾʿʼʸ

4.05

ʼƔʸʾ

ʻʿƔʹʿʷˀʺʽ

ʷƔʸʷʾʷʿʸʽʷʺ

ʸʹ

ʹƔʼʷˀʷˀʾʻʻʻ

Mean

0.054

Standard Deviation

ʷƔʷʹʿʻ

WŽƉƵůĂƚŝŽŶ ϭ WŽƉƵůĂƚŝŽŶ Ϯ

WŽƉƵůĂƚŝŽŶ ϯ WŽƉƵůĂƚŝŽŶ ϰ

0 0.02

0.04

0.06

0.08

0.10

0.12

Distribution of Values 0.12

0.10

Twelve individuals of population 4 was formed by using the specific breeding strategy and taking two descendents from the previous population. The individuals were evaluated and ranked over the same fitness criteria. The individual fitness of the population had reduced due to the strategies with respect to the previous population but the overall fitness (mean value) of the population had increased.

Mean

Standard deviation

Population 1 :

0.0474

0.0235

Population 2 :

0.0489

0.0175

Population 3 :

0.0400

0.0178

Population 4 :

0.054

0.0284

0.08

0.06

0.04

0.02

Architectural Association School of Architecture

SEQUENCE 1 - Population4A

mutated (rep)

mutated (inv)

P04.04 G04.04

F02 B02 E02 G01

P04.03 G04.03

E02 E02

P04a.10 G04a.10

B03

B02 E04 E02 G01

ÃÅº»È·Ê» ŰÊ

P04a.09 G04a.09

E02 G01 B02 E04 F02

P04a.08 G04a.08

G01

B01 E01 D01

P04a.07 G04a.07

E01

B01

P04a.06 G04a.06

ÃÅº»È·Ê» ŰÊ

ŰÊ

G01

mutated (add) ŰÊ

mutated (add)

ÃÅº»È·Ê» ŰÊ

P04a.05 G04a.05

mutated (ins)

D01 B01 E04 F02

B02 E04 F02

D01

ÃÅº»È·Ê» ŰÊ

P04a.04 G04a.04 Descendents

Descendents

mutated (ins)

mutated (add)

Population 4

G01 G01

ŰÊ

ÃÅº»È·Ê» ŰÊ

mutated (add)

P04a.03 G04a.03

E02 G01 G01

P04a.02 G04a.02

G01

P04a.01 G04a.01

B01 E01 D01 E04 E03

B01 E01 D01

ŰÊ

ÄŰÊ

POPULATION 4A

mutated (inv)

Population 4a

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

Architectural Association School of Architecture

SEQUENCE 1 - Population 4A

Mutation Stage

Breeding and Mutation Strategy

M2 - Monster

Parents

Mutation

A1 - Average 1

1 Pair

Non Mutated

50 % R P3.12 Rank 7th Fitness 0.048 A2 - Average 2

Non Mutated

P3.09 50 % R Rank 7th Fitness 0.044 A2 - Average 2

M1 - Monster

A3 - Average 3

P3.01 50 % R Rank 15th Fitness 0.078

1 Pair A1 - Average 1

Descendents

M3 - Monster

M1 - Monster

Population 4

M2 - Monster P3.07 Rank 14th Fitnes 0.074

Descendents 3 Pair

Inversion

Repetition 50 % R

25 %

M3 - Monster

Addition

P3.01 30 % R Rank 15th Fitness 0.078

25 %

A1 - Average

Insertion

P3.12 70 % R Rank 7th Fitness 0.048

25 %

Mutation takes place in animal and human bodies naturally. It effects the genome structure , and produces changes in the gene sequence, thus effecting the physical form of a body. In nature, mutation is rare and it does not effect every individual in a partocular popultion. Once effected by mutation, the individual has high chances of passing the mutated genes to the next generations. Mutation as a strategy was applied to 40% of the population and to 25% of the genes of the genome, in order to produce phenome with variations. Four types of mutation i.e inversion, repetition, addition and insertion were applied to the population randomly. 30

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

SEQUENCE 1 - Population4A

P04a.01 G04a.01

P04a.07 G04a.07

Architectural Association School of Architecture

P04a.02 G04a.02

P04a.08 G04a.08

P04a.03 G04a.03

P04a.04 G04a.04

P04a.09 G04a.09

P04a.10 G04a.10

P04a.05 G04a.05

P04a.06 G04a.06

P04.03 G04.03

P04.04 G04.04

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

SEQUENCE 1 - Population4A

Normal Distribution WŽƉƵůĂƚŝŽŶ ϭ

Population 04a

Height(H)

Total Surface Area(TSA)

H/TSA

Rank

Normal Distribution

4.03

Ϯϳϵ͘ϴϭϱϰϭϴ

Ϭ͘ϬϭϳϴϲϴϵϮϮ

ϭϯ

ϯ͘ϱϮϲϮϳϲϭϱϱ

4.04

ϳ͘ϲ

ϯϰϲ͘ϴϱϬϭϵϳ

Ϭ͘ϬϮϭϵϭϭϰϳϳ

ϭϭ

ϱ͘ϴϭϯϭϭϬϯϴϯ

4a.01

ϲ͘ϯϮ

Ϯϱϳ͘ϰϵϳϯϯϳ

Ϭ͘ϬϮϰϱϰϯϵϰϯ

ϳ͘ϳϰϲϵϰϮϭ

4a.03

ϲ͘ϵϴ

ϭϭϭ͘ϰϱϬϵϵϳ

Ϭ͘ϬϮϳϲϭϴϵϱϱ

ϭϬ͘ϰϮϳϴϬϰϭϳ

4a.04

ϲ͘ϭ

ϭϮϲ͘ϯϲϴϮϭϯ

Ϭ͘ϬϯϭϭϳϴϮϭϯ

ϭϯ͘ϵϳϬϰϱϬϳϱ

4a.06

ϭϬ͘ϲ

Ϯϳϭ͘ϭϵϯϭϮϭ

Ϭ͘ϬϯϵϬϴϲϱϯϳ

Ϯϭ͘ϵϱϰϮϮϬϱϮ

4a.1

ϱ͘ϴϲ

ϭϮϬ͘ϳϴϴϲϴϮ

Ϭ͘Ϭϰϴϱϭϰϰϳϵ

Ϯϲ͘ϯϰϱϲϵϵϲϯ

4a.02

ϲ͘ϱϯ

ϭϯϰ͘ϭϬϵϬϬϵ

Ϭ͘Ϭϰϴϲϵϭϳϯϯ

Ϯϲ͘ϯϯϴϭϳϳϲϰ

4a.08

ϲ͘ϱϰ

ϭϯϰ͘ϭϬϵϬϬϵ

Ϭ͘ϬϰϴϳϲϲϮϵϵ

Ϯϲ͘ϯϯϯϵϯϱϯϮ

4a.09

ϭϭ͘ϳ

Ϯϯϵ͘ϮϭϲϬϭϱ

Ϭ͘ϬϰϴϵϬϵϳϲϵ

Ϯϲ͘ϯϮϯϵϳϴϰϳ

4a.05

ϭϱ͘ϯ

ϮϵϬ͘ϮϰϴϱϮϱ

Ϭ͘ϬϱϮϳϭϯϰϰϲ

Ϯϱ͘ϮϮϭϲϵϳϬϴ

4a.07

ϱ͘Ϯϵ

ϴϵ͘ϬϵϲϳϭϮ

Ϭ͘Ϭϱϵϯϳϯϲϴϰ

ϭϮ

ϮϬ͘ϭϬϭϮϴϳϴϱ

»·Ä

ʷƔʷʺˀ

Ê·Äº·Èº »Ì¿·Ê¿ÅÄ

ʷƔʷʸʺˀ

WŽƉƵůĂƚŝŽŶ Ϯ WŽƉƵůĂƚŝŽŶ ϯ

WŽƉƵůĂƚŝŽŶ ϰ WŽƉƵůĂƚŝŽŶ ϰĂ Ɖ

0.02

0.04

0.06

0.08

0.10

Distribution of Values 0.12

0.10

The population 4A was created by following the same strategies for breeding and killing. 40% of the individuals were selected randomly to mutate. They were evaluated and ranked over the same fitness criteria. We observed that after mutating the overall fitness (mean value) and the individual fitness of the population had reduced drastically .

Mean

Standard deviation

Population 1 :

0.0474

0.0235

Population 2 :

0.0489

0.0175

Population 3 :

0.0400

0.0178

Population 4 :

0.054

0.0284

Population 4A : 0.039

0.0139

0.08

0.06

0.04

0.02

Architectural Association School of Architecture

0.12

SEQUENCE 1 - Population 5

mutated (inv)

mutated (dup)

mutated (rep)

mutated (inv)

P05.15 G05.15

G01 E02 B02 F02

P05.10 G05.10

E02 E02 E02

P04a.10 G04a.10

ÃÅº»È·Ê» ŰÊ

B03

B02 E04 E02 G01

ÃÅº»È·Ê» ŰÊ

P04a.09 G04a.09

E02 G01 B02 E04 F02

P04a.08 G04a.08

G01

B01 E01 D01

P04a.07 G04a.07

E01

B01

P04a.06 G04a.06

ŰÊ

mutated (add) ŰÊ

mutated (add)

Descendents

mutated (ins)

mutated (add)

Population 4

G01

ÃÅº»È·Ê» ŰÊ

mutated (ins) ÃÅº»È·Ê» ŰÊ

Descendents

P04a.05 G04a.05

D01 B01 E04 F02

B02 E04 F02

D01

ŰÊ

ÃÅº»È·Ê» ŰÊ

P04a.04 G04a.04

G01 G01

mutated (dup)

mutated (add) ŰÊ

ÄŰÊ

P05.02 G05.02

E02 E02 G01 G01 G01

P05.01 G05.01

E03 E04 D01 E01 E01

POPULATION 5

mutated (inv)

Population 4a

Population 5

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

P05.01 G05.01

P05.02 G05.02

P04a.04 G04a.04

P04a.05 G04a.05

P04a.08 G04a.08

P04a.09 G04a.09

P04a.10 G04a.10

P05.10 G05.10

30 Architectural Association School of Architecture

P04a.06 G04a.06

Population 05

Height(H)

Total Surface Area(TSA)

H/TSA

Rank

Normal Distribution

5.10

ϰϭϬ͘ϳϴϮϭϯϭ

Ϭ͘ϬϭϮϭϳϭϵϬϮ

ϭϭ

ϭ͘ϱϰϰϱϴϱϵϴϵ

4a.06

ϭϬ͘ϲ

Ϯϳϭ͘ϭϵϯϭϮϭ

Ϭ͘ϬϯϵϬϴϲϱϯϳ

Ϯϭ͘ϵϱϰϮϮϬϱϮ

5.11

ϭϮ͘ϰ

ϯϭϬ͘ϴϰϵϱϮϴ

Ϭ͘ϬϯϵϴϵϬϲϴϯ

ϮϮ͘ϲϯϴϮϭϯϰϳ

4a.04

ϲ͘ϭ

ϭϮϲ͘ϯϲϴϮϭϯ

Ϭ͘ϬϰϴϮϳϭϲϯϯ

Ϯϲ͘ϯϱϬϭϰϰϭϭ

4a.10

ϱ͘ϴϲ

ϭϮϬ͘ϳϴϴϲϴϮ

Ϭ͘Ϭϰϴϱϭϰϰϳϵ

Ϯϲ͘ϯϰϱϲϵϵϲϯ

4a.08

ϲ͘ϱϰ

ϭϯϰ͘ϭϬϵϬϬϵ

Ϭ͘ϬϰϴϳϲϲϮϵϵ

Ϯϲ͘ϯϯϯϵϯϱϯϮ

4a.09

ϭϭ͘ϳ

Ϯϯϵ͘ϮϭϲϬϭϱ

Ϭ͘ϬϰϴϵϬϵϳϲϵ

Ϯϲ͘ϯϮϯϵϳϴϰϳ

4a.05

ϭϱ͘ϯ

ϮϵϬ͘ϮϰϴϱϮϱ

Ϭ͘ϬϱϮϳϭϯϰϰϲ

Ϯϱ͘ϮϮϭϲϵϳϬϴ

4a.07

ϱ͘Ϯϵ

ϴϵ͘ϬϵϲϳϭϮ

Ϭ͘Ϭϱϵϯϳϯϲϴϰ

ϮϬ͘ϭϬϭϮϴϳϴϱ

5.01

ϮϮ͘ϲϱ

ϯϲϮ͘ϯϭϯϲϱϰ

Ϭ͘ϬϲϮϱϭϰϴϵϱ

ϭϲ͘ϴϴϳϵϱϮϳϰ

5.02

ϭϭ͘Ϯϱ

ϭϱϵ͘ϴϵϰϱϮϱ

Ϭ͘ϬϳϬϯϱϴϴϴϮ

ϭϬ

ϵ͘ϬϱϴϯϮϱϴϲϱ

»·Ä

ʷƔʷʻʿ

Ê·Äº·Èº »Ì¿·Ê¿ÅÄ

ʷƔʷʸʼʸ

P04a.07 G04a.07

Distribution of Values

Normal Distribution 30

WŽƉƵůĂƚŝŽŶ ϱ

0.08 0.07

0.06 20

0.05 0.04

0.03

0.02 5

P05.15 G05.15

0.01

0 0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

SEQUENCE 1 - Conclusion

P. 01 19

P. 02 Introduce 5 new genes

P. 03 Lost 14 genes

P. 04 Mutation added 2 genes

P. 04a

P. 05 Lost 54 % of the genes overall from P.02

Mean WŽƉƵůĂƚŝŽŶ ϭ 25

Population 1 :

0.0474

0.0235

Population 2 :

0.0489

0.0175

Population 3 :

0.0400

0.0178

Highest

Population 4 :

0.0540

0.0284

Lowest

Population 4a :

0.0390

0.0139

Population 5 :

0.0480

0.0151

WŽƉƵůĂƚŝŽŶ Ϯ WŽƉƵůĂƚŝŽŶ ϯ WŽƉƵůĂƚŝŽŶ ϰ WŽƉƵůĂƚŝŽŶ ϰĂ WŽƉƵůĂƚŝŽŶ ϱ

Standard deviation

The number of points defined for the instructions were plenty and the population looked similar due to the selection of the genes and the gene structure. Tracking down the genes through the population we realised that we had lost more than fifty percent of the genes by the third population itself .This was due to the random picking of genes and the specific strategies. The strategies proved that the populations with increase in individual fitness's but reduced overall fitness (mean value) of the population could be generated by this logic.

0 0.02

0.04

Architectural Association School of Architecture

0.06

0.08

0.1

0.12

SEQUENCE 1 - Population 1-5

Descendents

Åº¿Ű»º

mutated (inv)

Åº¿Ű»º

mutated (rep) ÄŰÊ

mutated (rep)

ÄŰÊ

mutated (inv)

ŰÊ

ÃÅº»È·Ê» ŰÊ

ŰÊ

Åº¿Ű»º ŰÊ

mutated (add) ŰÊ

Non mutated

Primitive

Åº¿Ű»º ÄŰÊ

mutated (add)

ŰÊ

mutated (ins) ÃÅº»È·Ê» ŰÊ

Åº¿Ű»º ŰÊ

Descendents

ÄŰÊ Descendents

Descendents

ŰÊ

ÄŰÊ

Population 1

Population 2

Population 3

ÃÅº»È·Ê» ŰÊ

ŰÊ

ÃÅº»È·Ê» ŰÊ

mutated (ins)

mutated (add) ŰÊ

mutated (add)

ÄŰÊ

Population 4

mutated (rep)

mutated (inv)

Population 4a

mutated (inv)

Population 5 38

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

Architectural Association School of Architecture

SEQUENCE 2

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

Architectural Association School of Architecture

SEQUENCE 2 - Strategies b

Body Plan

New Genes B

Move

Scale

Centriod

South

Scale

North

Rotate

B01

M(xY)(a/2)

B04

M(Xy)(a/2)

B05

M(xZ)(a/4)

E01A

S1(Xy)(PC,0.5)

E04A

S1(Xz)(PC,0.75)

E07A

S1(Yz)(PC,1.5)

F03A

S2(xy)(PC,1.2)

F05

S2(yz)(PC,1.2)

F06

S3(xyz)(PC,1.5)

G01

R(xz)(PC,15)

G05

R(xy)(PC,45)

G06

R(yz)(PC,330)

Enviornmental Factor Evaluation criteria :

Surface area facing south Surface area facing north

Surface Maximizes sunlight captured

North face

x>0

y<0

y>0

z >0

South face

In sequence two, the body plan was redefined by splitting it into two halves, Part A and Part B and a new gene structure was created. In this sequence we had to consider an environmental factor for which we defined our growth pattern accordingly. We were interested in looking at maximising the south surface for Part B and developing the Part A as a support system. The instructions were defined with the centroid of each part respectively simplifying the system and ease for naming the set of instructions. Gene pool was created consisting of 4 families and various specific set of instructions under each of them as "gene". An energy source was considered from the south direction. All the south facing surfaces were taken into consideration and compared over the surfaces facing the north. The north facing and south facing surfaces were evaluated in the Rhino environment based on their surface normals. Any face that had vector x > 0 ,vector y < 0 ,vector z > 0 was considered as a north facing surface and any face that had vector x > 0 ,vector y > 0 ,vector z > 0 was considered as a south facing surface. Thus individuals with more south facing surfaces over the north facing surfaces were considered as fit individuals.

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

SEQUENCE 2 - Strategies Gene Structure

Genome 1

Genome 2

Genome 3

Genome 4

Genome 5

Genome 6

Genome 7

Genome 8

Genome 9

Genome 10

Genome 11

Genome 12

a a

Part A (north)

Growth pattern : a + 2b

Architectural Association School of Architecture

Part B (south)

In sequence 2, the maximum number of gene in a genome was restricted to 6. Desiring the Part B to grow twice with respect to the Part A and defining the growth pattern as a + 2b,the first half of the genes in the genome to Part A and the next half of the genes to Part B and repeated it in the same order to have differential growth.

SEQUENCE 2 - Strategies Killing strategy

Breeding Strategy

A1 - Average 1 A2 - Average 2

A1 - Average 1 50 % 1 Pair A2 - Average 2

A3 - Average 3

50 % M1 - Monster C

M1 - Monster

M2 - Monster

50 % M3 - Monster

1 Pair D

M2 - Monster

Descendents

50 %

Descendents E

M3 - Monster 30 % 3 Pair

A3 - Average 70 %

The individuals selected for bredding process were of average fitness and low fitness. 5 pairs were formed by combining the selected individual as shown in the fig. Two individuals were selected as descendants to move to the next population without breeding them.

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

SEQUENCE 2 - Breeding Strategy

Genome 1

Genome 2

Architectural Association School of Architecture

Breeding Strategy In order to have consistency in the strategies and be able compare them we continued the same breeding and killing strategies for all the populations in sequence 2. The first individual was formed by the process of breeding and taking the head of the first parent and the tail of the second parent. The next individual was generated doing exactly the opposite process. Thus, with this bredding strategy it was made sure that the first three gene of an individual always formed the first three gene of the next individual.

SEQUENCE 2 - Population 6 P06.01 G06.01

B04

B01

F03A

G06

E07A

F05

G06

E07A

F05

P06.02 G06.02

B04

B01

F03A

E01A

G01

F03A

E01A

G01

P06.03 G06.03

E07A

B05

G06

F05

E04A

B04

F05

E04A

B04

P06.04 G06.04

B01

G06

B04

F06

G05

E04A

F06

G05

E04A

P06.05 G06.05

B05

E07A

B01

G06

F06

E01A

G06

F06

E01A

P06.06 G06.06

E07A

B01

B04

E01A

G01

F06

E01A

G01

F06

P06.07 G06.07

G06

F06

B04

B05

G01

E07A

B05

G01

E07A

P06.08 G06.08

E01A

B04

G01

F03A

G05

B05

F03A

G05

B05

P06.09 G06.09 P06.10 G06.10 P06.11 G06.11 P06.12 G06.12 Population 6

B01

B04

B01

B05

G06

F06

G05

F03A

E04A

G06

F05

G05

B04

E07A

B05

B04

F03A

B01

E01A

B01

G01

E04A

G06

F06

B04

E07A

B05

B04

F03A

B01

E01A

B01

G01

E04A

G06

F06

B01

M(xY)(a/2)

B04

M(Xy)(a/2)

B05

M(xZ)(a/4)

E01A

S1(Xy)(PC,0.5)

E04A

S1(Xz)(PC,0.75)

E07A

S1(Yz)(PC,1.5)

F03A

S2(xy)(PC,1.2)

F05

S2(yz)(PC,1.2)

F06

S3(xyz)(PC,1.5)

G01

R(xz)(PC,15)

G05

R(xy)(PC,45)

G06

R(yz)(PC,330) 46

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

SEQUENCE 2 - Population 6

P06.01 G06.01

P06.02 G06.02

P06.03 G06.03

P06.04 G06.04

P06.05 G06.05

P06.06 G06.06

P06.07 G06.07

P06.08 G06.08

P06.09 G06.09

P06.10 G06.10

P06.11 G06.11

P06.12 G06.12

Mean : 1.1057 Population 06 6.01 6.02 6.03 6.04 6.05 6.06 6.07 6.08 6.09 6.1 6.11 6.12

Normal Distribution SA2ͲSouth ϵϵ͘ϭϱϱϮϵϵϱ ϲϰ͘ϵϬϲϯϵϳϴ ϵϴ͘ϯϲϮϱϬϵϲ ϭϭϴ͘ϮϴϵϳϳϮ ϭϮϭ͘ϴϵϯϯϯ ϯϲ͘ϰϱϬϱϳϮϲ ϲϬ͘Ϭϴϯϯϯϰϲ ϭϱϵ͘ϰϮϮϵϬϭ ϭϬϵ͘ϴϳϯϳϭϯ ϭϮϰ͘ϱϵϲϱϬϯ ϱϱ͘ϲϵϬϭϳϳϳ Ϯϰϰ͘ϲϯϳϱϵ

SA1ͲNorth ϭϱϴ͘ϴϵϰϯϮϮ ϵϭ͘ϰϮϭϭϯϯϮ ϳϰ͘ϯϬϮϮϭϵ ϴϱ͘ϭϳϴϴϭϬϴ ϭϬϴ͘ϱϭϴϴϬϵ ϵϰ͘ϰϵϯϲϱϱϳ ϲϱ͘ϴϰϴϬϴϳϵ ϭϬϳ͘ϲϱϲϮϱϳ ϵϱ͘ϱϳϳϰϳϯϭ ϴϴ͘ϯϵϵϱϳϰϭ ϰϬ͘ϯϵϯϮϲϴϰ ϭϳϲ͘ϴϵϮϯϲ

SA2/SA1 Ϭ͘ϲϮϰϬϯϮϵϵϰ Ϭ͘ϳϬϵϵϳϭϰϴϲ ϭ͘ϯϮϯϴϭϲϱϴϮ ϭ͘ϯϴϴϳϮϮϵϴ ϭ͘ϭϮϯϮϰϲϭϬϵ Ϭ͘ϯϴϱϳϰϲϮϰϮ Ϭ͘ϵϭϮϰϱϯϳϰϴ ϭ͘ϰϴϬϴϱϭϮϯϰ ϭ͘ϭϰϵϱϳϳϱϬϰ ϭ͘ϰϬϵϰϲϵϰϵϰ ϭ͘ϯϳϴϲϵϵϰϲϵ ϭ͘ϯϴϮϵϳϰϯϭϯ

Rank ϭϭ ϭϬ ϲ ϯ ϴ ϭϮ ϵ ϭ ϳ Ϯ ϱ ϰ

ϭ͘Ϯ

hŶĨŝƚ hŶĨŝƚ ĞƐ &ŝƚ hŶĨŝƚ

&ŝƚ &ŝƚ ĞƐ

Population 6 was generated with new set of genes but with the same breeding and killing strategies as in the previous sequence. These individuals were evaluated with new fitness criteria of Surface area facing south / Surface area facing north and ranked accordingly. The result showed a population well distributed along the bell curve due to the new fitness criteria and the gene structure.

Ϭ͘ϴ

Ϭ͘ϲ

Ϭ͘ϰ

Ϭ͘Ϯ

Ϭ Ϭ

Architectural Association School of Architecture

Ϭ͘Ϯ

Ϭ͘ϰ

Ϭ͘ϲ

Ϭ͘ϴ

ϭ͘Ϯ

ϭ͘ϰ

ϭ͘ϲ

SEQUENCE 2 - Population 6A P06a.01 G06a.01

B01

G06

B04

F06

E01A

G01

F06

E01A

G01

P06a.02 G06a.02

B04

B01

F03A

G05

E04A

F03A

G05

E04A

P06a.03 G06a.03

B01

G05

F05

B05

E07A

F05

B05

E07A

F05

P06a.04 G06a.04

B04

B01

F03A

G06

E01A

G06

E01A

G06

P06a.05 G06a.05

B01

G06

E04A

B04

G01

F06

B04

G01

F06

P06a.06 G06a.06

E07A

B01

B04

E01A

F03A

G01

E01A

F03A

G01

P06a.07 G06a.07

B01

G06

E04A

B05

E01A

G06

B05

E01A

G06

P06a.08 G06a.08

B01

G05

F05

B04

F03A

G01

B04

F03A

G01

ŰÊ

ÄŰÊ

Descendents

ŰÊ

ÄŰÊ

ŰÊ

ÄŰÊ

ŰÊ

P06a.09 G06a.09 ÄŰÊ

P06a.10 G06a.10

B04

E07A

B01

F03A

B04

E01A

G06

G01

E07A

F06

F05

E01A

G06

G01

E07A

F06

F05

ÄŰÊ

P06.12 G06.12

P06.03 G06.03 Descendents

Population 6

Population 6a

B05

E07A

F03A

B05

G05

G06

B04

F05

B01

E04A

F06

B04

F05

B01

E04A

F06

B04

B01

M(xY)(a/2)

B04

M(Xy)(a/2)

B05

M(xZ)(a/4)

E01A

S1(Xy)(PC,0.5)

E04A

S1(Xz)(PC,0.75)

E07A

S1(Yz)(PC,1.5)

F03A

S2(xy)(PC,1.2)

F05

S2(yz)(PC,1.2)

F06

S3(xyz)(PC,1.5)

G01

R(xz)(PC,15)

G05

R(xy)(PC,45)

G06

R(yz)(PC,330) 48

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

SEQUENCE 2 - Population 6A

P06a.01 G06a.01

P06a.02 G06a.02

P06a.03 G06a.03

P06a.04 G06a.04

P06a.07 G06a.07

P06a.08 G06a.08

P06a.09 G06a.09

P06a.10 G06a.10

Mean : 1.3681 Population 06a 6a.01 6a.02 6a.03 6a.04 6a.05 6a.06 6a.07 6a.08 6a.09 6a.10 6.12 6.03

P06a.05 G06a.05

P06a.06 G06a.06

P06.12 G06.12

P06.03 G06.03

Normal Distribution SA2ͲSouth ϭϮϰ͘ϱϳϱϱϯϭ ϲϳ͘ϳϬϰϯϲϳϵ ϭϯϬ͘ϱϱϯϮϬϭ ϲϮ͘ϵϯϯϲϵϮϱ ϮϮϯ͘ϱϰϴϵϮϯ ϰϮ͘ϯϮϯϮϱϲϭ ϲϮ͘ϲϯϰϰϯϳ ϭϲϰ͘Ϯϭϱϱϳϭ ϭϮϳ͘ϲϱϴϵϱϭ ϭϬϭ͘ϯϱϯϮϴϵ Ϯϰϰ͘ϲϯϳϱϵ ϵϴ͘ϯϲϮϱϬϵϲ

SA1ͲNorth ϳϰ͘ϰϮϭϰϰϵϭ ϭϭϬ͘ϯϱϳϬϮ ϰϳ͘Ϭϱϯϱϰϭϵ ϳϯ͘ϵϲϮϰϴϭϱ ϭϰϰ͘ϵϭϭϭϱϭ ϰϳ͘ϴϬϭϬϮϵϱ ϲϮ͘ϯϵϵϳϱϳϵ ϴϬ͘ϯϬϮϬϮϳϯ ϴϬ͘ϲϮϱϭϳϰϱ ϭϯϳ͘ϯϴϵϯϴϲ ϭϳϲ͘ϴϵϮϯϲ ϳϰ͘ϯϬϮϮϭϵ

SA2/SA1 ϭ͘ϲϳϯϵϭϵϳϭϳ Ϭ͘ϲϭϯϱϬϯϬϰϲ Ϯ͘ϳϳϰϱϲϲϵϮϰ Ϭ͘ϴϱϬϴϴϲϳϬϵ ϭ͘ϱϰϮϲϲϭϵϳϵ Ϭ͘ϴϴϱϰϬϰϲϵϴ ϭ͘ϬϬϯϳϲϬϴϵϴ Ϯ͘ϬϰϰϵϳϰϭϲϮ ϭ͘ϱϴϯϯϲϯϰϬϳ Ϭ͘ϳϯϳϳϬϴϮϵ ϭ͘ϯϴϮϵϳϰϯϭϯ ϭ͘ϯϮϯϴϭϲϱϴϮ

Rank ϯ ϭϮ ϭ ϭϬ ϱ ϵ ϴ Ϯ ϰ ϭϭ ϲ ϳ

Ϭ͘ϳ

&ŝƚ hŶĨŝƚ hŶĨŝƚ &ŝƚ

Ϭϲ Ϭ͘ϲ

Ϭ͘ϱ

Ϭ͘ϰ

ĞƐ hŶĨŝƚ ĞƐ &ŝƚ

With the same strategies population 6A was generated. Evaluating and ranking them according to criteria a diverse population distributed along the curve was observed but the individual fitness of the population had reduced increasing the overall fitness of the population.

Ϭ͘ϯ

Ϭ͘Ϯ

Ϭ͘ϭ

Ϭ Ϭ

Architectural Association School of Architecture

Ϭ͘ϱ

ϭ͘ϱ

Ϯ͘ϱ

SEQUENCE 2 - Population 7 P07.01 G07.01

B01

G06

B04

F06

E01A

G01

F06

E01A

G01

P07.02 G07.02

B04

B01

F03A

G06

E01A

G01

G06

E01A

G01

P07.03 G07.03

B01

G06

E04A

B04

E07A

F05

B04

E07A

F05

P07.04 G07.04

E07A

B01

B04

G06

G01

F06

G06

G01

F06

P07.05 G07.05

E07A

B05

G06

F05

G05

E04A

F05

G05

E04A

P07.06 G07.06

B04

B01

F03A

E04A

B04

F03A

E04A

B04

P07.07 G07.07

E07A

B05

G06

B04

G01

F06

B04

G01

F06

P07.08 G07.08

B01

G06

E04A

F05

E04A

B04

F05

E04A

B04

ŰÊ

ÄŰÊ

ŰÊ

ÄŰÊ

Des

ÄŰÊ

Des

ŰÊ

P07.09 G07.09 Descendents

ÄŰÊ

P07.10 G07.10

E07A

B04

B01

B04

B01

F03A

G06

G05

E07A

E04A

F05

F03A

G06

G05

E07A

E04A

F05

ÄŰÊ

P06a.09 G06a.09

B04

B01

F03A

E01A

G01

F06

E01A

G01

F06

Descendents

P06.12 G06.12 Population 6a

Population 7

B05

F03A

G05

B04

B01

F06

B04

B01

F06

B01

M(xY)(a/2)

B04

M(Xy)(a/2)

B05

M(xZ)(a/4)

E01A

S1(Xy)(PC,0.5)

E04A

S1(Xz)(PC,0.75)

E07A

S1(Yz)(PC,1.5)

F03A

S2(xy)(PC,1.2)

F05

S2(yz)(PC,1.2)

F06

S3(xyz)(PC,1.5)

G01

R(xz)(PC,15)

G05

R(xy)(PC,45)

G06

R(yz)(PC,330) 50

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

SEQUENCE 2 - Population 7

P07.01 G07.01

P07.02 G07.02

P07.03 G07.03

P07.04 G07.04

P07.07 G07.07

P07.08 G07.08

P07.09 G07.09

P07.10 G07.10

Mean : 1.17 Population 07 7.01 7.02 7.03 7.04 7.05 7.06 7.07 7.08 7.09 7.10 7.11 7.12

P07.05 G07.05

P07.06 G07.06

P06a.09 G06a.09

P06.12 G06.12

Normal Distribution SA2ͲSouth ϭϮϰ͘ϱϬϱϬϳϴ ϱϵ͘ϴϭϯϰϮϯϮ ϭϱϰ͘ϵϯϭϭϱϲ ϭϰϴ͘ϴϵϰϴϭϯ ϯϳ͘ϲϯϯϱϭϲϱ ϵϬ͘ϱϳϲϬϯϮϲ ϮϬϭ͘ϳϵϲϲϱϯ ϵϵ͘ϯϬϭϯϵϭϭ ϰϱ͘ϬϴϱϲϯϭϮ ϭϭϴ͘ϲϬϯϱϰϳ ϭϮϳ͘ϲϱϴϵϱϭ Ϯϰϰ͘ϲϯϳϱϵ

SA1ͲNorth ϵϰ͘ϲϱϲϭϯϭ ϳϳ͘Ϯϭϲϭϳϰϱ ϳϰ͘ϭϬϰϯϯϮϳ ϭϴϬ͘ϰϮϬϭϭ ϲϬ͘ϴϰϯϵϯϬϮ ϴϱ͘Ϭϱϴϵϲϯϴ ϭϲϬ͘ϲϲϬϲϴϯ ϲϬ͘Ϭϲϲϳϱϯϳ ϵϰ͘ϲϱϮϮϳϲϵ ϭϭϳ͘ϳϰϱϬϭϴ ϴϬ͘ϲϮϱϭϳϰϱ ϭϳϲ͘ϴϵϮϯϲ

SA2/SA1 ϭ͘ϯϭϱϯϰϬϴϳϯ Ϭ͘ϳϳϰϲϮϮϵϴ Ϯ͘ϬϵϬϳϭϲϱϲϲ Ϭ͘ϴϮϱϮϲϳϮϳϴ Ϭ͘ϲϭϴϱϮϱϰϬϰ ϭ͘Ϭϲϰϴϲϭϲϵϴ ϭ͘ϮϱϲϬϰϮϱϰϰ ϭ͘ϲϱϯϭϴϯϵϭϲ Ϭ͘ϰϳϲϯϮϵϬϳϮ ϭ͘ϬϬϳϮϵϭϰϮϱ ϭ͘ϱϴϯϯϲϯϰϬϳ ϭ͘ϯϴϮϵϳϰϯϭϯ

Rank ϱ ϭϬ ϭ ϵ ϭϭ ϳ ϲ Ϯ ϭϮ ϴ ϯ ϰ

&ŝƚ hŶĨŝƚ

Ϭ͘ϴ

Ϭ͘ϳ

hŶĨŝƚ &ŝƚ ĞƐ

Ϭ͘ϲ

hŶĨŝƚ ĞƐ &ŝƚ ĞƐ

Ϭϰ Ϭ͘ϰ

Being consistent with the well distributed population along the bell curve just like the previous two populations we evaluated and ranked them with the same strategies and fitness criteria. The overall fitness of the population reduced increasing the individual fitness's of the population.

Ϭ͘ϱ

Ϭ͘ϯ

Ϭ͘Ϯ

Ϭ͘ϭ

Ϭ Ϭ

Architectural Association School of Architecture

Ϭ͘ϱ

ϭ͘ϱ

Ϯ͘ϱ

Normal Distribution Comparison ϭ͘Ϯ

WŽƉƵůĂƚŝŽŶ ϲ WŽƉƵůĂƚŝŽŶ ϲĂ WŽƉƵůĂƚŝŽŶ ϳ

Mean Population 6

1.1057

Population 6a :

1.3681

Population 7

1.17

Ϭ͘ϴ

Comparing the normal distribution of population 6, 6A and 7, it can be seen that the mean value in population 6A is the highest. There are lot more individuals around the mean value, hence the bell curve is flatter. Population 6 has few individual near the mean value and few less fit individual, thus the graph has the highest peak. By comparing the normal distribution graph, the fitness level between the individuals can be compared and also the fitness of the population can be estimated.

Ϭ͘ϲ

Ϭ͘ϰ

Ϭ͘Ϯ

Ϭ Ϭ

Ϭ͘ϱ

ϭ͘ϱ

Ϯ͘ϱ

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

SEQUENCE 2 - Population 8

Genome 1

In hierarchical assembly we bred the parents with nested genes by taking the head of the first parent and the tail of the second parent. Doing the exact opposite procedure we produced the second individual. Making sure we applied the first half of the genes in the genome to Part A and the next half of the genes to Part B and repeated it in the same order to have differential growth. Nested gene structure was designed such that, 2nd instruction followed the 1st instruction and the 3rd instruction was applied on the both instruction together and not only on the 2nd.

Genome 2

Heirarchial assembly

Architectural Association School of Architecture

SEQUENCE 2 - Population 8 P08.01 G08.01

B04

B01

F03A

E01A

G01

E01A

G01

P08.02 G08.02

B04

B01

F03A

G06

G01

F06

G06

G01

F06

P08.03 G08.03

B01

G06

E04A

F06

G05

E04A

F06

G05

E04A

P08.04 G08.04

E07A

B05

G06

F05

E01A

G06

F05

E01A

G06

P08.05 G08.05

B04

B01

F03A

G05

E04A

F03A

G05

E04A

P08.06 G08.06

E07A

B01

B04

F03A

E04A

B04

F03A

E04A

B04

P08.07 G08.07

B04

B01

F03A

F05

G05

E04A

F05

G05

E04A

P08.08 G08.08

E07A

B05

G06

E01A

G01

G06

E01A

G01

Des

ÄŰÊ

ŰÊ

ÄŰÊ

Fit

ÄŰÊ

Descendents

ÄŰÊ

P08.09 G08.09

E07A

B05

G06

F03A

G05

E04A

F03A

G05

E04A

ÄŰÊ

P08.10 G08.10

E07A

B01

B04

F05

G05

E04A

F05

G05

E04A

ÄŰÊ

P06.12 G06.12

P07.07 G07.07 Descendents

Population 7

Population 8

B05

E07A

F03A

B05

G05

G06

B04

B01

G01

F06

B04

B01

G01

F06

B01

M(xY)(a/2)

B04

M(Xy)(a/2)

B05

M(xZ)(a/4)

E01A

S1(Xy)(PC,0.5)

E04A

S1(Xz)(PC,0.75)

E07A

S1(Yz)(PC,1.5)

F03A

S2(xy)(PC,1.2)

F05

S2(yz)(PC,1.2)

F06

S3(xyz)(PC,1.5)

G01

R(xz)(PC,15)

G05

R(xy)(PC,45)

G06

R(yz)(PC,330) 54

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

SEQUENCE 2 - Population 8

P08.01 G08.01

P08.02 G08.02

P08.03 G08.03

P08.04 G08.04

P08.07 G08.07

P08.08 G08.08

P08.09 G08.09

P08.10 G08.10

Mean : 0.7653 Population 08 8.01 8.02 8.03 8.04 8.05 8.06 8.07 8.08 8.09 8.10 6.12 6b.07

P08.05 G08.05

P08.06 G08.06

P06.12 G06.12

P07.07 G07.07

Normal Distribution Comparison SA2ͲSouth ϱϭ͘ϴϴϮϮ ϭϱϰ͘ϱϵϭϱ ϴϭ͘ϲϰϯϳ ϰϵ͘ϭϬϵϰ ϳϴ͘Ϭϴϭϰ ϴϯ ϳϰϰϵ ϴϯ͘ϳϰϰϵ ϲϮ͘ϯϳϯϮ ϯϭ͘ϱϵϰϯ ϯϮ͘ϱϵϰϲ ϳϳ͘ϰϰϬϳ Ϯϰϰ͘ϲϯϳϲ ϮϬϭ͘ϳϵϲϳ

SA1ͲNorth ϴϳ͘Ϯϰϭϭ ϭϴϮ͘ϱϱϮϳ ϭϰϴ͘ϳϯϳϳ ϱϲ͘ϵϳϰϱ ϭϮϱ͘ϯϲϳϵ ϭϬϴ ϴϵϰϲ ϭϬϴ͘ϴϵϰϲ ϵϰ͘ϴϳϱϬ ϲϬ͘ϰϲϰϰ ϲϵ͘ϭϬϳϬ ϭϭϵ͘ϯϳϬϮ ϭϳϲ͘ϴϵϮϰ ϭϲϬ͘ϲϲϬϳ

SA2/SA1 Ϭ͘ϱϵϰϳ Ϭ͘ϴϰϲϴ Ϭ͘ϱϰϴϵ Ϭ͘ϴϲϮϬ Ϭ͘ϲϮϮϴ Ϭ ϳϲϵϬ Ϭ͘ϳϲϵϬ Ϭ͘ϲϱϳϰ Ϭ͘ϱϮϮϱ Ϭ͘ϰϳϭϳ Ϭ͘ϲϰϴϳ ϭ͘ϯϴϯϬ ϭ͘ϮϱϲϬ

Rank ϵ ϰ ϭϬ ϯ ϴ ϱ ϲ ϭϭ ϭϮ ϳ ϭ Ϯ

ϭ͘ϲ

ĞƐ hŶĨŝƚ &ŝƚ

ϭ͘Ϯ

&ŝƚ ĞƐ hŶĨŝƚ hŶĨŝƚ &ŝƚ

After breeding individuals with nested genes and we evaluated all the population on the same fitness criteria. We noticed that the individual fitness's of most of the individuals had reduced drastically with respect to the previous population and the overall fitness (mean value) of the population had also reduced this was due to the nested genes condition we created for this population. Architectural Association School of Architecture

ϭ͘ϰ

Ϭ͘ϴ

WŽƉƵůĂƚŝŽŶ ϲ Ϭ͘ϲ

WŽƉƵůĂƚŝŽŶ ϲĂ WŽƉƵůĂƚŝŽŶ ϳ

Ϭ͘ϰ

WŽƉƵůĂƚŝŽŶ ϴ

Ϭ͘Ϯ

Ϭ Ϭ

Ϭ͘ϱ

ϭ͘ϱ

Ϯ͘ϱ

SEQUENCE 2 - Population 6-8

Descendents

ŰÊ

Des

ÄŰÊ

ŰÊ

ÄŰÊ

ŰÊ

Des

Fit

ÄŰÊ

ŰÊ

Des

ŰÊ

ÄŰÊ

Descendents

ÄŰÊ

Descendents

ÄŰÊ

Descendents

Population 6

Descendents

Population 6a

Population 7

Population 8

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

SEQUENCE 2 - Conclusions Gene Tracing

FIT

RANK: 3

P06.04 G06.04

UNFIT RANK: 10

RANK: 5

P06.02 G06.02

FIT

P06a.01 G06a.01

B04 B01 F03A G06 E01A G06 G06 E01A G06

FIT

RANK: 10

B04 B01 B04 F03A E01A G01

P06a.04 G06a.04

Preliminary Observation

UNFIT

RANK: 3

RANK: 12

B01 G06 B04 F06 E01A G01 F06 E01A G01

P06a.02 G06a.02

B04 B04 B01 F03A G05 E04A F03A G05 E04A

P06b.01 G06b.01

P06b.02 G06b.02

B01 G06 B04 F06 E01A G06 F06 E01A G06

B04 B01 F03A G06 E01A G01 G06 E01A G01

UNFIT

RANK: 11 P06b.05 G06b.05

RANK: 10

B Part

Gene

B Part

Gene

E07A B05 G06 F05 G05 E04A F05 G05 E04A

G05

Rotate Family

E04A

Scale 1D Family

B04

Copy Family

B04 B04 G05 E04A E01A G01

Des

P06.03 G06.03

RANK: 7

E07A B05 G06 F05 E04A B04 F05 E04A B04

P06b.06 G06b.06

FIT RANK: 7

B04 B04 B01 F03A E04A B04 F03A E04A B04

Creates Unfit Individual

B Part

Architectural Association School of Architecture

Creates Fit Individual

We tried to track down three generations of the fit and the unfit individual to observe and analyse their genomes. From our preliminary observation it showed that there were genes from a particular family in a certain position which made the individual fit or unfit respectively. Gene tracing although possible in computational world, is very difficult to be traced in natural world. In the natural world the genes structure is very complex and has evolved due to its conditions taking into account billions of years. It is practically very difficult to track down a role of a particulat gene in development of a phenome.

Gene Pool Comparison Sequence 1 P1

Sequence 2 P2

P4a

B01

P6a

C(0,0,0)

B01

M(xY)(a/4)

B04

B02

M(Xy)(-a/4)

B05

B03

M(xY)(a/2)

E01A

B04

M(Xy)(a/2)

E04A

Mi(xz)(P1,P3)

E07A

D01

PA(xz)(P4,3,90)

F03A

D02

PA(xy) [(a/4,0,0),4,180]

F05

D03

PA(xy)(P1,5,360)

F06

D04

PA(xy)(P5,5,360)

G01

E01

S1(Xy)(P0,0.5)

G05

E02

S1(Xz)(P3,1.5)

G06

E03

S1(xZ)(P0,4)

B01

M(xY)(a/2)

E04

S1(Xz)(P5,0.75)

B04

M(Xy)(a/2)

E05

S1(Xy)(P3,2)

B05

M(xZ)(a/4)

E06

S1(xY)(P3,0.6)

F01

S2(xz)(P3,1.5)

F02

F03

Lo o k i n g a t t h e g e n e p o o l o f Sequence 1 and Sequence 2 we observed that the we carry for warded all the genes in sequence 2 unlike sequence 1. Because of random selection, more than fifty percent of the genes were lost in the third population of sequence 1. This resulted in phenome, which had very little variation from its parents and phenome of previous population.

E01A

S1(Xy)(PC,0.5)

E04A

S1(Xz)(PC,0.75)

S2(xy)(P3,2)

E07A

S1(Yz)(PC,1.5)

F03

S2(xy)(P4,0.4)

S2(xy)(PC,1.2)

F04

S2(xz)(P5,0.8)

F03A F05

S2(yz)(PC,1.2)

G01

R(Xz)(P3,15)

F06

S3(xyz)(PC,1.5)

G02

R(Xz)(P3,45)

G01

R(xz)(PC,15)

G03

R(xZ)(P3,45)

G05

R(xy)(PC,45)

G04

R(Xz)(P4,270)

G06

R(yz)(PC,330)

The random selection of phenome for bredding resulted in loss of some genes which had potential of producing variation in sequence 1. By restricting the size of the genome and the type of gene, it was possible to conserve all genes in sequence 2.

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

Architectural Association School of Architecture

SEQUENCE 3

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

SEQUENCE 3 Inspiration

Support System

Maximize Surface Area

y axis

x axis

With the body plan considered in sequence 2 we looked at various natural systems for inspirations. We were particularly inspired by the growth of sunflower. In the hypothesis for sequence 3 we looked at the concept of differential growth of two parts within a body. Thus we tried to grow "Part B" for maximising the surface area for capturing sunlight and developing "Part A" as a support system for the same. The sunflower orients its petals along the source of sunlight and the stem helps in holding the flower tall.

Front Elevation

Side Elevation

Body Plan of Pyramid

Hypothesis

Maximize Surface Area

Support System a

Part A( North)

Part B (South)

Growth pattern : a + 2b

Architectural Association School of Architecture

Part A Support System

Part B

M a x i m i ze S u r fa c e A re a Facing South

SEQUENCE 3

This sequence was divided into two parts i.e. manual process and scripted which ran parallely. We modified our fitness criteria's according to the inputs received from Galapagos (scripted process). We ran a few experiments in Galapagos based on our previous fitness criteria from sequence 2 and realised that individuals tends to become flat due to the fitness criteria of surface area facing south / surface area facing north. It tried the maximise the surface area facing south while minimising the surface facing the north. Thus,we altered our fitness criteria according to the results and the new fitness criteria of Surface area facing South / Total surface area was considered. It was also observed during the digital experiments that a wide range of input data gave multiple possiblities of creating fit individual. The digital scripted experiments were not restricted to a particular value as in the manual experiments, but had the possibilty of choosing from a range of values.

Manual

Galapagos

1) Range 2) Fitness Criteria

North

South

South South

Galapagos Initial criteria Surface area facing south Surface area facing north

North

Change Revised Evaluation criteria

Surface area facing south Total Surface Area

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

SEQUENCE 3 - Enviornemental Factor

Part A (North)

Part B (South)

Evaluation criteria 2 : Surface area facing south

Evaluation criteria 1 : Overall Height (h)

Evaluation criteria 3 : Number of Faces facing south direction

Total Surface Area

Total Volume

x axis

Support System

b a

number of faces

Maximize Surface Area ea b

In order to have a differetial growth of two parts of the body, more fitness criteria were added. Three different fitness criteria were considered for sequence 3, in order grow individual parts for specialised purpose. As we desired the " Part A" to grow as a support system we considered maximising the ratio of overall height / Volume. The individual which maximises its height while reducing its volume was considered as the fittest of all the individuals. The role of the " Part B " was to maximise the Surface area to capture maximum sunlight. We considered two fitness criteria for this purpose; 1. Maximising the ratio of Surface area facing South to Total surface area and 2. Maximising the number of surfaces facing south. We tried to relate our fitness criteria's with our inspiration and extract information from sunflower for the same. Architectural Association School of Architecture

SEQUENCE 3 - Fitness Criteria Weightage

Criteria 2

Criteria 3

Criteria 1

Part B

Part A

Surface area facing south

Number of faces

Total Surface Area 50% weightage

Height Volume

20% weightage

30% weightage

In order to evaluate individuals on three different criteria, the concept of weightage was adopted.We gave the fitness criterias weightage and evaluated and ranked the individuals accordingly. In the natural world the organism specialises its parts for specific function but grows as a whole system. To incorporate this we gave weightage to each criteria and evaluated the individual over its overall performance and then ranked them accordingly. The weightage was based the importance of the criteria and the further experiments the weightage of each criteria was also altered and result were observed.

100%

Overall Evaluation

Rank

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

SEQUENCE 3 - Revised Breeding Strategy

Population 7

Copy Family Gene Part A

Part B

a 5

Inverse order

a 2

b 2

After observing the genome of individuals of population 7, the importance of gene belonging to copy family was realised. It was also observed that continuing with the same bredding strategy will result in individuals of less fitness. Thus, it was decided to inverse the order of the genes in the genome of the individuals in order to achieve differential growth within a body and increase the fitness level of individuals in future populations.

b 8

Genome 2

a 6

Inverse order

b 5

Genome 1

After analysing population 7 and realising the presence of a dominant gene in "Part A" of the genome, it was important to revise the breeding strategy. The earlier breeding strategy resulted in individuals of lesser fitness in population 8.

Genome 2

a 1

Genome 1

Inverse order

b 12

Breeding Strategy a 4

b 5

b 8

Inverse order C Architectural Association School of Architecture

a 9

b 2

Inverse order

SEQUENCE 3 - Revised Mutation Strategy

a Inversion

Repetition 25 %

Addition 25 %

Insertion 25 %

a 4

We revised our mutation strategy and randomly mutated only 30% of the overall population through random selection. Only two mutation, inversion and repetition were used for the future population. Only twenty five percent of the genes in the genome were applied mutation through random selection. The results of this mutation strategy were observed in comparison to the earlier results.

b 3

b 6

a 4

b 3

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

SEQUENCE 3 - Population 9

Mean :

P09.01 G09.01

P09.02 G09.02

P09.03 G09.03

P09.04 G09.04

P09.07 G09.07

P09.08 G09.08

P09.09 G09.09

P09.10 G09.10

P09.05 G09.05

P09.06 G09.06

P06.12 G06.12

P07.07 G07.07

0.4522

Standard deviation : 0.0426

Population 09 9.01 9.02 9.03 9.04 9.05 9.06 9.07 9.08 9.09 9.10 6.12 7.07

SA2ͲSouth ϭϭϴ͘ϳϭϲϱ ϭϯϲ͘ϲϭϵϰ ϭϱϰ͘ϱϴϳϳ ϳϰ͘Ϯϰϴϱ ϭϲϭ͘Ϯϵϴϭ ϭϳϳ͘Ϯϱϴϴ ϭϯϲ͘ϬϬϱϰ ϳϲ͘Ϯϳϱϵ ϵϮ͘ϭϱϬϯ ϭϳϮ͘ϴϬϲϱ ϭϵϱ͘ϮϰϯϬ Ϯϰϰ͘ϲϯϳϲ

Architectural Association School of Architecture

TSA Ϯϳϭ͘ϳϴϮϰ ϯϰϰ͘ϬϬϴϰ ϯϬϭ͘ϵϭϯϱ ϭϳϱ͘ϳϮϵϭ ϯϰϴ͘ϴϵϱϴ ϰϮϯ͘ϵϬϵϴ Ϯϴϳ͘ϴϲϱϱ ϭϳϭ͘ϯϳϯϴ ϮϬϬ͘ϲϴϳϲ ϯϯϵ͘ϮϬϵϵ ϯϲϮ͘ϰϱϳϯ ϰϮϭ͘ϱϯϬϬ

Height (h) ϭϯ͘ϳϮϬϯ ϭϭ͘ϮϱϬϬ ϱ͘ϬϬϬϬ ϳ͘ϲϭϲϬ ϳ͘ϲϭϲϬ ϱ͘ϬϬϬϬ ϱ͘ϬϬϬϬ ϱ͘ϬϬϬϬ ϳ͘ϲϭϲϬ ϭϭ͘ϵϯϬϯ ϳ͘ϲϰϲϱ ϱ͘ϭϴϲϮ

Volume (v) ϭϯϴ͘ϯϴϰϮ ϭϳϯ͘ϳϳϳϬ ϭϳϬ͘Ϭϵϵϲ ϭϬϲ͘ϴϯϲϯ ϵϰ͘ϯϯϮϱ ϭϮϯ͘ϳϮϯϬ Ϯϭϳ͘ϵϵϯϮ ϭϰϰ͘ϰϵϯϳ ϵϲ͘ϴϬϬϳ ϭϯϳ͘ϱϵϱϳ ϭϲϵ͘ϲϴϱϯ ϭϭϯ͘ϳϵϮϲ

Faces ϭϭ ϭϮ Ϭ͘ϬϮϵϰ ϭϯ Ϭ͘Ϭϳϭϯ ϭϱ Ϭ͘ϬϴϬϳ ϭϳ Ϭ͘ϬϰϬϰ ϭϮ Ϭ͘ϬϮϮϵ ϭϭ Ϭ͘Ϭϯϰϲ ϭϲ Ϭ͘Ϭϳϴϳ ϭϲ ϭϲ Ϭ͘Ϭϰϱϭ ϭϯ ϭϮ

Part B Criteria 1 (SA2/TSA) Ϭ͘ϰϯϲϴ Ϭ͘ϯϵϳϭ Ϭ͘ϱϭϮϬ Ϭ͘ϰϮϮϱ Ϭ͘ϰϲϮϯ Ϭ͘ϰϭϴϮ Ϭ͘ϰϳϮϱ Ϭ͘ϰϰϱϭ Ϭ͘ϰϱϵϮ Ϭ͘ϱϬϵϰ Ϭ͘ϱϯϴϳ Ϭ͘ϱϴϬϰ

Part B Criteria 2 (faces) Ϭ͘ϱϱ Ϭ͘ϲ Ϭ͘ϲϱ Ϭ͘ϳϱ Ϭ͘ϴϱ Ϭ͘ϲ Ϭ͘ϱϱ Ϭ͘ϴ Ϭ͘ϴ Ϭ͘ϴ Ϭ͘ϲϱ Ϭ͘ϲ

Part A Criteria 3 (h/v) Ϭ͘Ϭϵϵϭ Ϭ͘Ϭϲϰϳ Ϭ͘ϬϮϵϰ Ϭ͘Ϭϳϭϯ Ϭ͘ϬϴϬϳ Ϭ͘ϬϰϬϰ Ϭ͘ϬϮϮϵ Ϭ͘Ϭϯϰϲ Ϭ͘Ϭϳϴϳ Ϭ͘Ϭϴϲϳ Ϭ͘Ϭϰϱϭ Ϭ͘Ϭϰϱϲ

Overall Ϭ͘ϰϬϯϮ Ϭ͘ϯϵϭϱ Ϭ͘ϰϱϲϵ Ϭ͘ϰϱϬϱ Ϭ͘ϱϬϮϯ Ϭ͘ϯϵϳϮ Ϭ͘ϰϬϱϴ Ϭ͘ϰϲϵϱ Ϭ͘ϰϴϱϯ Ϭ͘ϱϭϮϭ Ϭ͘ϰϳϯϯ Ϭ͘ϰϳϵϯ

Rank ϭϬ ϭϮ ϳ ϴ Ϯ ϭϭ ϵ ϲ ϯ ϭ ϱ ϰ

SEQUENCE 3 - Population 9A Repetition

P09.01 G09.01

Inversion

P09a.02 G09a.02

P09a.03 G09a.03

P09.04 G09.04

P09.05 G09.05

P09.06 G09.06

Repetition

P09.07 G09.07

Mean :

P09.08 G09.08

P09.09 G09.09

P09.10 G09.10

Inversion

P09a.11 G09a.11

P09a.12 G09a.12

0.4217

Standard deviation : 0.0655

Population 09a 9.01 9a.02 9a.03 9.04 9.05 9.06 9.07 9.08 9.09 9.10 9a.11 9a.12

SA2ͲSouth ϭϭϴ͘ϳϭϲϱ Ϯϱϰ͘ϳϲϳϲ ϲϰ͘ϲϭϴϰ ϳϲ͘ϰϴϭϵ ϭϲϭ͘Ϯϵϴϭ ϭϳϳ͘Ϯϱϴϴ ϭϯϲ͘ϬϬϱϰ ϳϲ͘Ϯϳϱϵ ϵϮ͘ϭϱϬϯ ϭϳϮ͘ϴϬϲϱ Ϯϱϵ͘ϱϱϲϬ ϭϯϳ͘Ϯϰϰϲ

TSA Ϯϳϭ͘ϳϴϮϰ ϳϵϬ͘Ϯϳϰϵ Ϯϯϱ͘ϭϰϮϬ ϭϳϱ͘ϳϮϵϭ ϯϰϴ͘ϴϵϱϴ ϰϮϯ͘ϵϬϵϴ Ϯϴϳ͘ϴϲϱϱ ϭϳϭ͘ϯϳϯϴ ϮϬϬ͘ϲϴϳϲ ϯϯϵ͘ϮϬϵϵ ϱϰϬ͘ϵϭϯϯ ϯϲϰ͘ϵϱϬϳ

Height (h) ϱ͘ϭϵ ϭϳ͘ϮϬ ϭϬ͘ϰϭ ϳ͘ϲϮ ϱ͘ϬϬ ϱ͘ϬϬ ϱ͘ϬϬ ϳ͘ϲϮ ϳ͘ϲϮ ϱ͘ϬϬ ϭϭ͘Ϯϱ ϴ͘ϵϵ

Volume (v) ϭϭϯ͘ϳϵϮϲ ϴϲϳ͘Ϯϴϱϳ ϭϮϱ͘ϰϬϯϯ ϵϲ͘ϴϬϬϳ ϭϰϰ͘ϰϵϯϳ Ϯϭϳ͘ϵϵϯϮ ϭϮϯ͘ϳϮϯϬ ϵϰ͘ϯϯϮϱ ϭϬϲ͘ϴϯϲϯ ϭϳϬ͘Ϭϵϵϲ ϯϵϬ͘ϴϲϯϯ Ϯϰϴ͘ϳϲϰϮ

Faces ϭϮ ϭϰ ϴ ϭϳ ϭϲ ϭϭ ϭϮ ϭϳ ϭϱ ϭϯ ϭϰ ϭϭ

Part B Criteria 1 (SA2/TSA) Ϭ͘ϰϯϲϴ Ϭ͘ϯϮϮϰ Ϭ͘Ϯϳϰϴ Ϭ͘ϰϯϱϮ Ϭ͘ϰϲϮϯ Ϭ͘ϰϭϴϮ Ϭ͘ϰϳϮϱ Ϭ͘ϰϰϱϭ Ϭ͘ϰϱϵϮ Ϭ͘ϱϬϵϰ Ϭ͘ϰϳϵϴ Ϭ͘ϯϳϲϭ

Part B Criteria 2(faces) Ϭ͘ϲ Ϭ͘ϳ Ϭ͘ϰ Ϭ͘ϴϱ Ϭ͘ϴ Ϭ͘ϱϱ Ϭ͘ϲ Ϭ͘ϴϱ Ϭ͘ϳϱ Ϭ͘ϲϱ Ϭ͘ϳ Ϭ͘ϱϱ

Part A Criteria 3(h/v) Ϭ͘Ϭϰϱϲ Ϭ͘Ϭϭϵϴ Ϭ͘ϬϴϯϬ Ϭ͘Ϭϳϴϳ Ϭ͘Ϭϯϰϲ Ϭ͘ϬϮϮϵ Ϭ͘ϬϰϬϰ Ϭ͘ϬϴϬϳ Ϭ͘Ϭϳϭϯ Ϭ͘ϬϮϵϰ Ϭ͘ϬϮϴϴ Ϭ͘Ϭϯϲϭ

Overall Ϭ͘ϰϬϳϱ Ϭ͘ϯϳϱϮ Ϭ͘ϮϳϰϬ Ϭ͘ϰϴϴϯ Ϭ͘ϰϳϴϭ Ϭ͘ϯϳϴϳ Ϭ͘ϰϮϰϯ Ϭ͘ϰϵϯϳ Ϭ͘ϰϲϴϴ Ϭ͘ϰϱϱϲ Ϭ͘ϰϱϱϳ Ϭ͘ϯϲϬϯ

Rank ϴ ϭϬ ϭϮ Ϯ ϯ ϵ ϳ ϭ ϰ ϲ ϱ ϭϭ 68

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

SEQUENCE 3 - Population 10

P10.01 G10.01

P10.02 G10.02

P10.03 G10.03

P10.04 G10.04

P10.05 G10.05

P10.06 G10.06

P10.07 G10.07

P10.08 G10.08

P10.09 G10.09

P10.10 G10.10

P09.09 G09.09

P09.10 G09.10

Mean :

0.3353

Standard deviation : 0.1020

Population 10 10.01 10.02 10.03 10.04 10.05 10.06 10.07 10.08 10 09 10.09 10.10 9.09 9.10 M

SA2ͲSouth TSA ϭϮϳ͘ϯϭϵϲ Ϯϵϲ͘ϵϭϳϴ ϰϭϵ͘ϰϵϵϮ ϴϱϬ͘ϲϴϴϮ ϴϯ͘Ϭϱϱϵ Ϯϲϰ͘ϬϱϬϭ ϵϳϰ͘ϭϰϯϬ ϮϭϭϮ͘ϳϭϳϯ ϭϰϰ͘ϲϰϴϬ ϱϱϭ͘Ϯϱϴϭ ϭϰϯ͘ϭϰϴϯ ϯϲϱ͘ϴϳϲϮ ϴϳ͘ϯϴϵϱ ϮϮϯ͘Ϭϰϵϳ ϭϲϰ͘ϮϵϬϬ ϰϯϵ͘ϬϵϲϮ ϵϵ͘ϲϱϳϯ ϵϵ ϲϱϳϯ ϯϱϴ ϯϱϴ͘ϭϯϮϬ ϭϯϮϬ ϯϲ͘ϴϵϰϯ ϭϴϯ͘ϭϮϯϱ ϵϮ͘ϭϱϬϯ ϮϬϬ͘ϲϴϳϲ Ϯϭϵ͘Ϭϭϴϯ ϰϱϬ͘ϵϵϱϴ 0 3353

Architectural Association School of Architecture

Height (h) ϱ͘ϬϬ ϭϰ͘Ϭϲ ϴ͘ϳϱ Ϯϱ͘ϯϭ ϭϭ͘Ϯϱ ϱ͘Ϯϳ ϴ͘ϭϳ ϵ͘ϵϵ ϭϮ ϭϮ͘ϱϴ ϱϴ ϳ͘ϯϴ ϳ͘ϲϮ ϳ͘ϳϳ

Volume (v) ϭϱϲ͘ϴϯϴϴ ϳϵϬ͘ϳϰϬϳ ϭϮϮ͘Ϭϵϵϲ ϯϰϱϱ͘ϳϮϯϮ ϰϲϯ͘ϰϳϬϲ ϭϲϵ͘ϰϱϮϭ ϭϮϵ͘ϰϮϯϮ Ϯϯϴ͘ϬϬϮϬ ϮϮϵ ϮϮϵ͘ϱϮϲϱ ϱϮϲϱ ϳϲ͘ϰϭϭϲ ϭϬϲ͘ϴϯϲϯ Ϯϰϭ͘ϵϲϱϯ

Faces ϭϬ ϴ ϭϬ ϭϬ ϲ ϭϳ ϭϰ ϭϱ ϱ ϰ ϭϱ Ϯϯ

Part B Criteria 1 (SA2/TSA) Ϭ͘ϰϮϴϴ Ϭ͘ϰϵϯϭ Ϭ͘ϯϭϰϱ Ϭ͘ϰϲϭϭ Ϭ͘ϮϲϮϰ Ϭ͘ϯϵϭϮ Ϭ͘ϯϵϭϴ Ϭ͘ϯϳϰϮ Ϭ Ϭ͘Ϯϳϴϯ Ϯϳϴϯ Ϭ͘ϮϬϭϱ Ϭ͘ϰϱϵϮ Ϭ͘ϰϴϱϲ

Part B Criteria 2(faces) Ϭ͘ϰ Ϭ͘ϯϮ Ϭ͘ϰ Ϭ͘ϰ Ϭ͘Ϯϰ Ϭ͘ϲϴ Ϭ͘ϱϲ Ϭ͘ϲ ϬϬ͘Ϯ Ϯ Ϭ͘ϭϲ Ϭ͘ϲ Ϭ͘ϵϮ

Part A Criteria 3(h/v) Ϭ͘Ϭϯϭϵ Ϭ͘Ϭϭϳϴ Ϭ͘Ϭϳϭϳ Ϭ͘ϬϬϳϯ Ϭ͘ϬϮϰϯ Ϭ͘Ϭϯϭϭ Ϭ͘Ϭϲϯϭ Ϭ͘ϬϰϮϬ ϬϬ͘Ϭϱϰϴ Ϭϱϰϴ Ϭ͘Ϭϵϲϲ Ϭ͘Ϭϳϭϯ Ϭ͘ϬϯϮϭ

Overall Ϭ͘ϯϰϬϴ Ϭ͘ϯϰϲϭ Ϭ͘Ϯϵϭϲ Ϭ͘ϯϱϮϬ Ϭ͘ϮϬϴϭ Ϭ͘ϰϬϱϴ Ϭ͘ϯϳϲϱ Ϭ͘ϯϳϱϱ ϬϬ͘ϮϭϬϭ ϮϭϬϭ Ϭ͘ϭϲϴϭ Ϭ͘ϰϮϯϴ Ϭ͘ϱϮϱϮ

Rank ϴ ϳ ϵ ϲ ϭϭ ϯ ϰ ϱ ϭϬ ϭϮ Ϯ ϭ

SEQUENCE 3 - Conclusion

Mean

Normal Distribution Comparison 9/9A/10

Population 9

0.4522

Population 9a :

0.4217

Population 10 :

0.3353

1.0

WŽƉƵůĂƚŝŽŶ 9 WŽƉƵůĂƚŝŽŶ 9a WŽƉƵůĂƚŝŽŶ 10

0.8

0.6

0.4

0.2

We generated population 9, 9A and 10 by using our revised breeding and mutation strategy. We evaluated them with three weighted criteria and ranked them accordingly. We realised that with this strategy the individual fitness's and the overall fitness (mean value) of the population kept on reducing consistently but we always had a population well distributed along the bell curve. Doing these tests manually parallel to the Galapagos experiments we understood the relation of fitness criteria's and importance of computational experiments which allow us to run many generation at one time. We ran various experimnts on Galapagos and tried to understand its potential and possibilities and the relation of the growth of "Part a" with respect to "Part b" in the next section of sequence three.

ϬϬϬϬ

Ϭ͘ϭϬϬϬ

Ϭ͘ϮϬϬϬ

Ϭ͘ϯϬϬϬ

Ϭ͘ϰϬϬϬ

Ϭ͘ϱϬϬϬ

Ϭ͘ϲϬϬϬ

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

SEQUENCE 3 - Galapagos Experiments Approach 1 M(xY)

M(xY)

M(Xy)

M(xZ)

S1(Xy)

S1(Xz)

S1(Yz) S2(xy) Part A

M(xY)

S1(Yz) S2(xy)

R(xy)

S1(Yz) S2(xy)

S1(Xz)

S1(Yz) S2(xy)

S2(yz)

S3(xyz)

R(xz)

R(xy)

R(yz)

S3(xyz)

Criteria 2 : Part B

Surface area facing south/ Total Surface Area 50%

PHENOME

Criteria 1 : Part A Height / Volume 30%

Criteria 3 : Part B

Part B

M(xY)

M(Xy)

M(xZ)

S1(Xy)

S1(Xz)

S1(Yz) S2(xy)

M(Xy)

S1(Yz) S2(xy)

R(yz)

S1(Yz) S2(xy)

S1(Xy)

S1(Yz) S2(xy)

S2(yz)

S3(xyz)

R(xz)

R(xy)

R(yz)

Architectural Association School of Architecture

Number of faces 20%

S2(yz)

Number of genes

Experiment 1 Experiment 2 Experiment 3

Criteria 1 Height/Volume

Criteria 2 SSA/TSA

Criteria 3 Number of faces

30%

50%

20%

30%

50%

20%

30%

50%

20%

Applied on entire geometry

In the book: Architecture of Emergence, Michael Weinstock mentions that "Random variation produces the raw material of variant forms, and natural selection acts as the force that chooses the forms that survive". In order to understand the concept of random variation and natural selection a series of experiments were conducted computationally in Galapagos. In the previous population experimentations, the variation in the form of the individual were minimal. Two different approaches were set up inorder to understand the concept. In the first approach, a list of instructions (gene) was applied to part A and part B of the primitive according to the previous body plan and the phenome was evaluated based on three weighted criteria as shown in the fig. Three sets of experiments were conducted with this approach and the number of genes was altered in each and the resulted were observed.

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

SEQUENCE 3 - Experiment 1 (Nos. of Gene 08)

0.175

0.072 1.2

1.1

0.337

Population 1

0.48

1.6

Population 3

Population 27

1.4

1.3

0.437

1.5

0.185

0.128

0.528

1.8

1.7

Population 52

Population 118

Fitness Criteria:

Unfit

0.07

0.125

0.391

0.493

0.492

1. South Surface Area/ Total Surface Area

Average

0.175

0.186

0.43

0.5

0.513

2. Height/Volume

Fittest

0.33

0.368

0.48

0.51

0.528

3.Number of Surfaces facing South direction

Architectural Association School of Architecture

SEQUENCE 3 - Experiment 2 (Nos. of Gene 10)

0.144

0.189

2.1

2.2

Population 1

0.404

2.6

2.5

Population 3

Population 27

2.4

2.3

0.389

0.385

0.375

0.257

0.428

2.7

Population 52

Population 90

2.8

Fitness Criteria:

Unfit

0.009

0.144

0.385

0.39

0.336

1. South Surface Area/ Total Surface Area

Average

0.189

0.257

0.3933

0.396

0.417

2. Height/Volume

Fittest

0.389

0.3896

0.394

0.40

0.428

3.Number of Surfaces facing South direction 74

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

SEQUENCE 3 - Experiment 3 (Nos. of Gene 12)

0.36

0.286

Population 3

Population 27

3.4

0.36

3.6

3.5

0.267

3.3

3.2

3.1

Population 1

0.181

0.133

0.016

0.399

3.7

Population 52

Population 64

3.8

Fitness Criteria:

ÄŰÊ

0.0169

0.225

0.286

0.346

0.371

1. South Surface Area/ Total Surface Area

Average

0.181

0.267

0.328

0.360

0.397

2. Height/Volume

Fittest

0.360

0.362

0.387

0.399

3.Number of Surfaces facing South direction

Architectural Association School of Architecture

SEQUENCE 3 - Galapagos Experiments Approach 1 : Analysis

Part B : Increases in Surface Area

The results of the three experiments were evaluated and analysed. From the three experiments, it can be observed that as the number of genes increased the fitness of the individual reduced and the mean value of the population also reduced. It can be observed from these results that, it is not necessary by increasing the number of genes in a genome the fitness level of the individual increases. It was also observed that, since the entire phenome was evaluated with all three criteria, part B grew in surface area while part A shrank. The idea behind the differential growth of the two body parts was that one grows twice then the other. Hence, it was necessary to conduct another series of experiment in order to ensure that both part of the body grew differentially.

Part A : Shrinks proportionately

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

SEQUENCE 3 - Galapagos Experiments Approach 2 M(xY)

M(xY)

M(Xy)

M(xZ)

S1(Xy)

S1(Xz)

S1(Yz)

S2(xy) Part A

M(xY)

S2(xy)

R(xy)

S2(xy)

S1(Xz)

S2(xy)

S2(yz)

S3(xyz)

R(xz)

R(xy)

R(yz)

Criteria 1 : Part A S3(xyz)

Height / Volume 30%

PHENOME

Total Surface Area

Criteria 2 : Part B

Part B

M(xY)

M(Xy)

M(xZ)

S1(Xy)

S1(Xz)

S1(Yz) S2(xy)

M(Xy)

S1(Yz) S2(xy)

R(yz)

S1(Yz) S2(xy)

S1(Xy)

S1(Yz) S2(xy)

S2(yz)

S3(xyz)

R(xz)

R(xy)

R(yz)

Architectural Association School of Architecture

SSA TSA 50%

Surface area facing south

S2(yz)

Criteria 3 : Part B

Number of faces 20%

Number of genes

Criteria 1

Criteria 2

Criteria 3

Height/Volume

SSA/TSA

Number of faces

Experiment 4

30%

50%

20%

Experiment 5

20%

60%

20%

Experiment 6

50%

40%

30%

Applied on Part A and Part B seperately

In the previous experiments, in the differential growth of the two body parts part B grew but part A shrank. Thus, a new approach was set up and three experiments were conducted in order to observe and understand the results. In these experiments, instead of evaluating the whole phenome with the three fitness criteria, each part is analysed for different performance. Thus, part A is evaluated for its height, while part b is evaluated for its surface area. The three criteria are weighted and the fitness level of individuals is noted. In these experiments the number of genes in each genome is kept same while the weightage of each criteria is changed with each experiment. The results of the experiments were noted and compared with the previous results.

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

SEQUENCE 3 - Experiment 4 (Nos. of Gene 10)

0.2878

0.230 4.2

4.1

1.040 4.5

0.409 4.3

1.485 4.6

0.4211 4.4

1.694 4.7

1.881 4.8

Fitness Criteria: Part A 1. Vertical Height/Horizontal Expansion (30%) Part B 2. South Surface Area/ Total Surface Area (50%) 3. Number of Surfaces facing South direction (20%)

Architectural Association School of Architecture

SEQUENCE 3 - Experiment 5 (Nos. of Gene 10)

0.210 5.1

1.527 5.6

0.404

5.3

5.2

1.516 5.5

0.358

0.274

5.4

2.032

2.261

5.7

5.8

Fitness Criteria: Part A 1. Vertical Height/Horizontal Expansion (30%) Part B 2. South Surface Area/ Total Surface Area (40%) 3. Number of Surfaces facing South direction (30%)

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

SEQUENCE 3 - Experiment 6 (Nos. of Gene 10)

0.141 6.1

0.184 6 .2

6.5

6.6

6.4

6.3

1.534

1.483

0.504

0.353

0.290

6.7

1.632 6.8

Fitness Criteria: Part A 1. Vertical Height/Horizontal Expansion (20%) Part B 2. South Surface Area/ Total Surface Area (60%) 3. Number of Surfaces facing South direction (20%)

Architectural Association School of Architecture

SEQUENCE 3 - Conclusion and Further development From experiments 4,5 and 6, it was observed that the weightage of fitness criteria plays an important role in defining the fitness level of individuals. It can seen from the experiments that by changing the percentage of fitness criteria, the fitness value of the population can be increased or decreased. During the process of experimentation, it was also observed that the order of the instructions plays an important role in the final results. It was observed that Galapagos, tends to neglect the instructions which area away from 0 in the list. Thus it is sometimes important to manipulate the list, in order to achieve desired results. It was also realised that for more accurate results it is important to run the experiments multiple number of times. In order to achieve higher level of accuracy while dealing with multiple fitness criteria, it is important to find the minimum and the maximum value of each fitness criteria and then weigh it.

Conclusion Nature works with the process of natural selection and we could relate the bits of code as the various "genes" of the individual. The evolutionary algorithm allows us to have the freedom of natural selection and form the genome for the individual and the result of the algorithm gives the phenome of the individual. We tried to explore the various breeding, killing and mutation strategies during the process and understand the relation of the fitness criteria's over these strategies over the various sets of populations.

Body Plan

We would like to study the concept of homeobox which plays an important role in growth of an individual, more into detail and explore its potentials and possibilities using the evolutionary algorithm as we did not explore this area much during this workshop. For further study we would like to redefine the body plan so as to relate to the natural system in terms of evolution of an organism in our case sunflower. We would also like to understand the various other environmental pressures associated with our natural system so that we could add more evaluation criteria's over the population.

One of our future goals would be to develope a psuedo code and modify it to link the embryological development and the evolution using the evolutionary algorithm. We had made our preliminary observations of gene tracing and it was made manually. We would be interested in understanding this concept computationally by running many generations and testing them methodically .

Sunflower

Pyramid

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

Architectural Association School of Architecture

Biblography

Books Sean B. Carroll. Endless Forms Most Beautiful-the New Science of EvoDevo and the Making of the Animal Kingdom. London: Quercus, 2011 Michael Hensel, Achim Menges and Michael Weinstock. Emergent Technologies and Design - Toward a biological paradigm for architecture. New York: Routledge, 2010 Thompson, D'Arch Wentworth. On Growth and Form. Cambridge: Cambridge UP, 1961 Weinstock, Michael. The Architecture of Emergence: The Evolution of Form in Nature and Civilisation. Chichester, U.K: Wiley, 2010

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia

Architectural Association School of Architecture

ARCHITECTURAL ASSOCIATION SCHOOL OF ARCHITECTURE GRADUATE SCHOOL PROGRAMMES COVERSHEET FOR SUBMISSION 2012-13 PROGRAMME: Emergent Technologies and Design TERM: 2 STUDENT NAME(S): Viral Doshi / Yan Bai / Tejas Sidnal / Jiangyue Xia SUBMISSION TITLE: Emergence Workshop COURSE TITLE : Emergence Workshop COURSE TUTOR: Michael Weinstock , George Jeronimidis , Evan Greenberg, Mehran Gharleghi SUBMISSION DATE: 11th Februrary 2013 DECLARATION: “I certify that this piece of work is entirely my/our own and that any quotation or paraphrase from the published or unpublished work of others is duly acknowledged.”

Signature of Student(s):

Viral Doshi

Yan Bai

Tejas Sidnal

Jiangyue Xia

Date: 11th of February 2013

Viral Doshi/Yan Bai / Tejas Sidnal /Jiangyue Xia