Emergence_AA EmTech

Emtech 2015-1016

Emergence seminar Documentation Emergent Technologies & Design FEBRUARY 2016 C.Lee D.Valdivia P.Boonthongrungtawee S.Arshad

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ACKNOWLEDGEMENTS COURSE DIRECTOR Michael Weinstock George Jeronimidis STUDIO MASTER Evan Greenberg TUTORS Mohammed Makki Elif Erdine DESIGN TEAM Diego Valdivia Ploy Banyaporn Boonthongrungtawee Syed Arshad Chien-Hao Lee

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ABSTRACT The aim of this research is to carry out a series of experiments, comprehending and applying the theory of embryological evolution and development into a digital space to create a series of computational iterative models, that are done in a 3D modelling (Rhino) as well as scripting environment (grasshopper).

The results are then analysed and evaluated according to an optimum value(maximum or minimum), referred to as Fitness criteria which will be the evolutionary goal.

This workshop is divided into four sequences, generating multiple populations in each. Sequences 1 and 2 deal with the manipulation of a simple geometric object through direct modelling commands.

Sequences 3 and 4 involve an urban block, which is modified through variations in its inherent geometric and spatial logics, and the iterations are created and analysed inside a visual scripting environment which ran a Genetic algorithm plugin.

Sequence 1 is started with a simple primitive (paraboloid) manipulated with a combination of geometric modifiers which forms the genome. The generated individuals are analysed and bred to further evolve generations.

In Sequence 2, the concept of a body plan is introduced in order to split the primitive and have part of the genome affect only certain parts of the body. Procedure of generating phenotypes is the same as above, but it was analysed for 2 fitness criteria.

Mutation was implemented like an artificial push to optimise the two fitness criteria.

Emergence Seminar Emergent Technologies & Design 2015-16

Sequence 3 onwards, the experiments are shifted to the evolution of an urban block using genetic algorithm. The Hutongs of Beijing are taken as a primitive and an associative model is defined. Multiple fitness criteria are defined based on environmental factors. The genetic algorithm runs iterations of the model and tests it against the three fitness criteria.

In multi parameter optimisation, the algorithm tries to push the best non-dominant solution into the next generation. In sequence 4, further complexity is added by grouping the urban blocks to create a super block.

The relationship between the blocks is an additional factor that influences gene action, thus creating a plethora of urban configurations After every generation and sequence, a comparative analysis is done on the data collected from the populations. In this manner it is possible to check whether the fitness values are approaching the optimum standards. Otherwise, its required to alter certain strategies.

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2.

3.

4.

ACKNOWLEDGEMENTS ABSTRACT TABLE OF CONTENT INTRODUCTION

5. SEQUENCE 1

5.1

5.2

5.3

5.4

6. SEQUENCE 2

5.1

5.2

5.3

5.4

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Primitive & Generation 1 Analysis of Phenotypes Generation 2 Breeding strategy/ Killing strategy/ Crossover strategy Effect and analysis of Phenotypes Generation 3 Breeding strategy/ Killing strategy/ Crossover strategy Effect and analysis of Phenotypes Comparative Analysis Normal distribution and Standard Deviation

10-13

Primitive & Generation 4 Analysis of Phenotypes Generation 5 Breeding strategy/ Killing strategy/ Crossover strategy Effect and analysis of Phenotypes Generation 6 Breeding strategy/ Killing strategy/ Crossover strategy Effect and analysis of Phenotypes Comparative Analysis Normal distribution and Standard Deviation

22-25

14-15 16-17 18-21

26-27 28-29 30-33

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TABLE OF CONTENT 7. SEQUENCE 3

5.1

5.2

5.3

5.4

8. SEQUENCE 4

5.1

5.2

5.3

5.4

Primitive & Generation 3 Analysis of Phenotypes Generation 6 Breeding strategy/ Killing strategy/ Crossover strategy Effect and analysis of Phenotypes Generation 10 Breeding strategy/ Killing strategy/ Crossover strategy Effect and analysis of Phenotypes Comparative Analysis Normal distribution and Standard Deviation

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Primitive & Generation 30 Analysis of Phenotypes Generation 62 Breeding strategy/ Killing strategy/ Crossover strategy Effect and analysis of Phenotypes Generation 152 Breeding strategy/ Killing strategy/ Crossover strategy Effect and analysis of Phenotypes Comparative Analysis Normal distribution and Standard Deviation

46-49

6. INFERENCES 7. REFERENCES

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50-51 52-53 54-57 58 59

INTRODUCTION The study of Evolutionary and Developmental processes within embryology is concerned with two inter related phenomena occurring over different time spans. One is concerned with the growth of an organism and the other is concerned with the evolution of the species through generations. The latter is a considerably slow process that is responsible for the emergence of new living forms. This is different from the evolution of other complex systems, in that the genome is transmitted down through generations.

[M.Weinstock, 2010]

Another important factor is Mutation, which is a random and permanent alteration to the genetic sequence of an organism. As random genetic mutations occur within an organism’s genetic code, the beneficial mutations are preserved because they aid survival -a process known as “natural selection.” These beneficial mutations are preserved in successive generations and when accumulated over a long period, it may give rise to an entirely new species. This is the premise of Darwin’s theory of evolution.

[J.Costa, 2009]

A critical concept of regulatory genes occurs in all living forms and these are common in all complex organisms, from insects to mammals. These emerged long before the evolution of complex organisms itself.

The so called tool kit or master genes orchestrate the formation and development of various parts in the ’body plan’ of an organism. It is done by specifically switching on and off the genes in different parts of the body and at different instances in time of the development process, which can lead to highly diverse forms. In addition to the switching on and off of genes , differential growth also occurs due to the stresses induced by external environmental forces.

[M.Weinstock, 2010][S.Carroll, 2005]

Evolutionary computation is a subset of artificial intelligence whose algorithms adopt Darwinian principles. Genetic Algorithms are adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetics mentioned above. It produces iterations that are evaluated through the definition of evolutionary goal or goals. Optimized solutions for a problem are generated using techniques like inheritance, crossbreeding,, selection and mutation.

[Doc.ic.ac.uk, 2016]

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SEQ 1 Sequence 1 is started with a simple primitive (paraboloid) manipulated with a combination of geometric modifiers which can be termed as the ’genepool’. 10 individuals are randomly produced in generation 1 each having a different sequence of modifiers or genes whose length is fixed at 6 numbers. The genes are defined with fixed intensities and are listed and given a letter and colour to identify them (fig. 1) The individuals in a generation are termed as ’Phenotypes’. The Fitness Criteria determined for this Sequence is to maximize the phenotypes Surface Area for their Volume. Once analysed and ranked according to the fitness criteria, some phenotypes are shortlisted according to our ’breeding strategy’ and give rise to the next generation based on the combinatorial logic of our ’crossover strategy’. In all, 3 generations are created in sequence 1, with a population of 10 in each.

Emergence Seminar Emergent Technologies & Design 2015-16

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SEQ 1 1 G.

STRATEGY GENE POOL

PRIMITIVE

A B C D E

Paraboloid

FITNESS CRITERIA

Copy 7.5 units (x-dir) Scale 1D = 3 times (z-dir local) Scale 3D = 0.5 Polar Array = 3 numbers XY plane(MP of edge) Rotate YZ plane 45 deg

To increase surface Area to Volume

fig. 01

fig. 02

BREEDING STRATEGY

KILLING STRATEGY

All genome are breed randomly

Fittest, Average, Less fit are selected for breeding, the rest of the population is “Killed”

MUTATION STRATEGY CROSSOVER STRATEGY

-

GENERATION 1 FINAL RESULT 1 2 3 4 5 6 (1.01) (1.02) (1.03) (1.04) (1.05) (1.06) (1.07) (1.08) (1.09) (1.10)

E B D B E A E C D

A E A A C B D D B

B A B D B C C A A

D D C A A D B B E

C C B B B E A C D

B E A D E A E B B

1 2 3 4 5 6 (1.01) (1.02) (1.03) (1.04) (1.05) (1.06) (1.07) (1.08) (1.09) (1.10)

E B D B E A E C D

A E A A C B D D B

B A B D B C C A A

D D C A A D B B E

C C B B B E A C D

B E A D E A E B B

fig. 03

Given the criteria, Generation 1 can be ranked according to individuals’ fitness value. Analyzing the phenotypes of Generation 1, a certain tendency towards flat and stretched shapes is present in the individuals who reach higher fitness values. While irregular and ‘inflated’ shapes tend to reach lower fitness values, as they increase their volume.

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From each fitness value obtained, a ‘Standard Deviation’ (SD) graph of the population can be elaborated. This graph plots a ‘Normal Distribution’ curve along SD values for each Generation. The SD Factor indicates by how much are the SD values spreading from the mean, which in the graph has the highest Normal Distribution value. The SD factor is hence an indicator of how much variation a population has: the higher the SD factor the more variation a generation has

GENERATION 1: RANDOM GEN. GENOME

SURFACE (cm.)

1.01 1.02 1.03 1.04 1.05 1.06 1.07 1.08 1.09 1.10

647.20 779.22 556.17 736.37 5266.19 599.76 558.02 1700.15 927.25 5117.62

Primitive EABDCB BEADCE DABCBA BADABD ECBABE ABCDEA EDCBAE CDABCB DBAEDB

VOLUME FIT1. S/V (cm.)

392.69 825.18 523.98 1227.56 22121.93 860.59 570.34 735.35 1739.23 12575.94

RANK

1.65 0.94 1.06 0.60 0.24 0.70 0.98 2.31 0.53 0.41

2 5 3 7 10 6 4 1 8 9 fig. 04

STANDARD DEVIATION

FREQUENCY OF GENES fig. 06

fig. 05

Mean fitness value = 0.944 SD Factor = 0.593

The Normal Distribution curve obtained from Generation 1 indicates that there is a low level of variation in the population, and the SD factor is 0.59 which is slightly closer to 1 than to 0. As observed in the process of transforming each individual, it was noticed that having gene ’D’ at the beginning of each sequence had no effect on the phenotype. Gene ’C’ is reducing the overall size of the genotypes, hence the ratio “Surface Area / Volume” will not change and the fitness criteria will not be affected. Therefore, the presence of gene ‘C’ in a genome is neutral and its high frequency in a population can be prejudicial in terms of variation.

SEQ 1 1 G.

FITNESS VALUES

G1

PHENOTYPES

th 1.02 (5 ) 0.94

Individual Fitness value Genome

1.01 1.65 Primitive

EABDCB

Individual Fitness value Genome

1.06 0.70

1.07 0.98

ECBABE

ABCDEA

1.03 1.06

BEADCE

ST 1.08 (1 ) 2.31

EDCBAE

1.04 0.60

1.05 (10th) 0.24

DABCBA

BADABD

1.09 0.53

1.10 0.41

CDABCB

DBAEDB fig. 07

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SEQ 1 2 G.

STRATEGY GENE POOL

PRIMITIVE

A B C D E

Paraboloid

FITNESS CRITERIA

Copy 7.5 units (x-dir) Scale 1D = 3 times (z-dir local) Scale 3D = 0.5 Polar Array = 3 numbers XY plane(MP of edge) Rotate YZ plane 45 deg

To increase surface Area to Volume

fig. 08

fig. 09

KILLING STRATEGY

BREEDING STRATEGY FITTEST

(1.08)

E D C B A E

(3)

LESS FIT

(1.05)

B A D A B D

(3)

GENERATION 1

Fittest, Average, Less fit are selected for breeding, the rest of the population is “Killed”

MUTATION STRATEGY -

OFFSPRING

E D C A B D

(6) fig. 10

CROSSOVER STRATEGY GENERATION 1

FINAL RESULT 1 2 3 4 5 6

1 2 3 4 5 6 (1.01) (1.02) (1.03) (1.04) (1.05) (1.06) (1.07) (1.08) (1.09) (1.10)

E B D B E A E C D

A E A A C B D D B

B A B D B C C A A

D D C A A D B B E

C C B B B E A C D

B E A D E A E B B

AVERAGE

B A D A B D

LESS FIT (3)

B A D A B D

FITTEST (3)

E D C B A E

(2.01) (2.02) (2.03) (2.04) (2.05) (2.06) (2.07) (2.08) (2.09) (2.10)

E D B B A E E D C B

D C B A E E D C B A

C B A E E D C B A E

A B D B A D B D B A

B C B A D A D B A D

D B A D A B B A D A

fig. 11

In order to breed Generation 2, a couple of individuals from Generation 1 were chosen according to a Breeding Strategy (fig.10) in which the fittest individual is mixed with the least fit one aiming more differentiation for next population. The genomes from these two individuals are quite different, so breeding them could hopefully result in a more variated set of genomes in the offspring. The selected Crossover Strategy picks 3 genes from each parent and shifts the number of gene selected by 1 unit on each sequence (fig.11). This strategy can assure that genomes from the offspring have 50% of genes from each parent but not in the same order of sequence, causing as well differentiation of phenotypes (fig.15).

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GENERATION 2: FITTEST AND LEAST FIT FROM G.1 GEN. GENOME

SURFACE (cm.)

2.01 2.02 2.03 2.04 2.05 2.06 2.07 2.08 2.09 2.10

3322.88 790.94 1762.37 5160.16 5511.55 7590.24 2515.23 599.76 1952.31 4471.69

EDCABD DCBBDB CBADBA BAEBAD AEEADA EEDDAB EDCBDB DCBDBA CBABAD BAEADA

VOLUME FIT1. S/V (cm.)

1460.89 1325.36 2870.40 19130.68 4069.91 5282.81 1114.84 860.58 2954.46 9694.99

RANK

2.27 0.60 0.61 0.27 1.35 1.44 2.26 0.70 0.66 0.46

1 8 7 10 4 3 2 5 6 9 fig. 12

STANDARD DEVIATION

FREQUENCY OF GENES fig. 13

Mean fitness value = 1.059 SD Factor = 0.689

fig. 14

The Normal Distribution graph from this Generation presents a displacement to the right compared to G1, at the same time, the curve is flattened and wider, meaning that more differentiation within the population was achieved. The increase in SD factor and Mean Fitness Value by 16% and 12 % respectively (compared to Generation 1, fig.13), supports the shift in the Normal Distribution curve. The Genes Frequency chart (fig.14) shows an increase in the presence of ‘D’ (Polar Array) and the opposite for ‘C’, which was described as a ’neutral’ gene in the previous analysis. The effect of gene ’D’ in the population, therefore seems to be beneficial for the fitness value as long as the phenotype’s volume remains low.

SEQ 1 2 G.

FITNESS VALUES

G1 G2

PHENOTYPES

Individual Fitness value Genome

Individual Fitness value Genome

ST 2.01 (1 ) 2.27

2.04 (10th) 0.27

2.02 0.60

2.03 0.61

DCBBDB

CBADBA

BAEBAD

AEEADA

2.06 1.44

2.07 2.26

2.08 0.70

2.09 0.66

2.10 0.46

EEDDAB

EDCBDB

DCBDBA

CBABAD

BAEADA

EDCABD

2.05 (5th) 1.35

fig. 15

Emergence Seminar Emergent Technologies & Design 2015-16

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SEQ 1 3 G.

STRATEGY FITNESS CRITERIA

GENE POOL

PRIMITIVE

A B C D E

Paraboloid

fig. 16

Copy 7.5 units (x-dir) Scale 1D = 3 times (z-dir local) Scale 3D = 0.5 Polar Array = 3 numbers XY plane(MP of edge) Rotate YZ plane 45 deg

fig. 17

BREEDING STRATEGY FITTEST AVERAGE LESS FIT FITTEST AVERAGE LESS FIT

GENERATION 1

GENERATION 2

(1.08) (1.02) (1.05) (2.01) (2.05) (2.04)

1 2 3 4 5 6 (3.01) (3.02) (3.03) (3.04) (3.05) (3.06) (3.07) (3.08) (3.09) (3.10)

E D C B A E E D C B

A B D C B E A B D C

D A B D B A D A B D

A B D E D C B D E D

D A A E E A D A E E

D B A E B A B A E B

A C B B D A

E B D D A D

E E B E A B

C B D C E E

B D A A A B

A C B B D A (3.10)

B D A A A B

(3.09)

C B D C E E

(3.08)

D A A D E A

(3.07)

E E B E A B

(3.06)

E B D D A D

(3.05)

A C B B D A

E A D A D D

(6)

E E B E A B

D A A D E A

C B D C E E

B D A A A B

A C B B D A

X

(3.04)

B D A A A B

(3.03)

C B D C E E

X

(3.02)

D A A D E A

E B D D A D

(1) (1) (1) (1) (1) (1)

D A A D E A

E B D D A D

30% DELETION

FINAL RESULT

1 2 3 4 5 6

1 2 3 4 5 6

D B A B A B

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

E D C B A E E D D B

A A D C B E A B B C

D B B D B A B A E D

A A D E D C D D E D

D B A E E A B A E E

D A E B A A B

fig. 19 16

Fittest, Average, Less fit are selected for breeding, the rest of the population is “Killed”

MUTATION STRATEGY 20% Deletion

fig. 18

(3.01)

E E B E A B

KILLING STRATEGY

X

OFFSPRING

CROSSOVER STRATEGY X

To increase surface Area to Volume

For Generation 3, the number of individuals selected to breed increased to 6 according to the new Breeding Strategy: the fittest; average fit and least fit individual from both Generation 1 and 2 (fig.18). The parents’ genes are to be combined following a new Crossover Strategy, which is taken as an experimental method. This time the offspring will contain one gene from each of the six parents, by first organizing them in a matrix and then making a diagonal selection starting from the first gene of the top row. The following sequence is obtained by shifting each row of the matrix one unit to the left (fig.19).

It’s appropriate to precise that this method of breeding is not natural in Biology, therefore it’s not particular from Genetic Algorithms either. This method is taken purely as an experiment to analyze the outcomes.

GENERATION 3: FITTEST AND LEAST FIT FROM G.2 GEN. GENOME

SURFACE (cm.)

3.01 3.02 3.03 3.04 3.05 3.06 3.07 3.08 3.09 3.10

5600.28 2402.69 514.06 191.61 6813.22 535.82 2402.69 2861.35 766.45 329.24

EADADD DBABAB CDBDAA BCDEEE ABBDEB EEACAA EADBDB DBADAA CDBEEE BCDDEB

VOLUME FIT1. S/V (cm.)

4684.64 7725.30 426.46 147.26 29390.30 189.47 7725.19 6010.04 1178.09 441.78

RANK

1.20 0.31 1.21 1.30 0.23 2.83 0.31 0.48 0.65 0.75

4 8 3 2 9 1 8 7 6 5 fig. 20

STANDARD DEVIATION

FREQUENCY OF GENES fig. 21

Mean fitness value = 0.927 SD Factor = 0.740

fig. 22

The Normal Distribution graph from this Generation presents a displacement to the right compared to G1, at the same time, the curve is flattened and wider, meaning that more differentiation within the population was achieved. The increase in SD factor and Mean Fitness Value by 16% and 12 % respectively (compared to Generation 1, fig.21), supports the shift in the Normal Distribution curve. The Genes Frequency chart (fig.22) shows an increase in the presence of ‘D’ (Polar Array) and the opposite for ‘C’, which was pointed as a ‘neutral’ gene in the previous analysis. The effect of gene ‘D’ in the population, therefore seems to be beneficial for the fitness value as long as the phenotype’s volume remains low.

SEQ 1 3 G.

FITNESS VALUES

G2 G3

PHENOTYPES

Individual Fitness value Genome

Individual Fitness value Genome

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3.01 1.20

3.02 0.31

3.03 1.21

3.04 1.30

EADADD

DABAB

CDBDAA

BCDEEE

3.07 0.31

3.08 0.48

3.09 0.65

EADBDB

DBDAA

CDBEEE

ST 3.06 (1 ) 2.83

EEACAA

Emergence Seminar Emergent Technologies & Design 2015-16

3.05 (9th) 0.23

ABBDEB

th 3.10 (5 ) 0.75

BCDDEB fig. 23 17

SEQ 1

STANDARD DEVIATION GRAPH COMPARISON The Total Gene Frequency chart compared to the ND curves, show that from G1 to G2 the variation increased as the frequency of Gene ’D’ increased and frequency of gene ’C’ decreased. The Polar Array operation (gene ’D’) creates differentiated individuals since it makes big changes in the phenotype shape and his fitness value. At the same time, as mentioned before, scaling down the overall shape (gene ’C’) has a null effect ona the individuals’ outcome of the fitness value, so including it in a genome is similar to reducing its length by one gene. Then by decreasing the frequency of ’C’ throughout the generations, more variation is obtained.

SD Values Actual values Mean fitness value SD Factor

G. 1 G. 1

G. 2 G. 2

G. 3 G. 3

0.944 0.593

1.059 0.689

0.927 0.740

STANDARD DEVIATION GRAPH COMPARISON

fig. 24

fig. 25

The most optimal shape according to the Fitness Criteria might be one with a flat, large configuration (fig.26). As mentioned previously, the primitive paraboloid has a proper ratio of “Surface area / Volume”, so trying to keep its original proportions and even multiplying will always be beneficial to the Fitness Criteria. Therefore, avoiding gene ‘B’ at the beginning of the sequences is convenient, since this gene maximizes the primitive Paraboloid’s volume by stretching it in z-direction. Analyzing the populations’ genomes in the three generations, shows that the least fit individuals contain ‘B’ genes in the first half of the sequence, therefore the fitness value reduces (fig.27)

Optimal Proportion shape

fig. 26 18

th 1.02 (5 ) 0.94

Individual Fitness value Genome

1.01 1.65 Primitive

EABDCB

Individual Fitness value Genome

1.06 0.70

1.07 0.98

ECBABE

ABCDEA

1.03 1.06

BEADCE

ST 1.08 (1 ) 2.31

EDCBAE

1.04 0.60

1.05 (10th) 0.24

DABCBA

BADABD

1.09 0.53

1.10 0.41

CDABCB

SEQ 1

G. 1

DBAEDB

G. 2 Individual Fitness value Genome

Individual Fitness value Genome

ST 2.01 (1 ) 2.27

2.04 (10th) 0.27

2.02 0.60

2.03 0.61

2.05 (5th) 1.35

EDCABD

DCBBDB

CBADBA

BAEBAD

AEEADA

2.06 1.44

2.07 2.26

2.08 0.70

2.09 0.66

2.10 0.46

EEDDAB

EDCBDB

DCBDBA

CBABAD

BAEADA

3.01 1.20

3.02 0.31

3.03 1.21

3.04 1.30

EADADD

DABAB

CDBDAA

BCDEEE

G. 3 Individual Fitness value Genome

Individual Fitness value Genome

ST 3.06 (1 ) 2.83

EEACAA

3.07 0.31

3.08 0.48

3.09 0.65

EADBDB

DBDAA

CDBEEE

3.05 (9th) 0.23

ABBDEB

th 3.10 (5 ) 0.75

BCDDEB fig. 27

Emergence Seminar Emergent Technologies & Design 2015-16

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SEQ 1 G. 1 Individual Fitness value Genome

ST 1.08 (1 ) 2.31

EDCBAE

th 1.02 (5 ) 0.94

EABDCB

1.05 (10th) 0.24

BADABD

G. 2 Individual Fitness value Genome

ST 2.01 (1 ) 2.27

EDCABD

2.03 (5th) 0.61

DCBDBA

2.04 (10th) 0.27

BAEBAD

G. 3 Individual Fitness value Genome

ST 3.06 (1 ) 2.82

th 3.10 (5 ) 0.74

EEACAA

CDDEB

3.05 (9th) 0.23

ABBDEB fig. 28

The fitness criteria is more surface area for less volume, which means physically the phenotype which is relatively smaller and flatter would have higher fitness value. In generation 1, from observation of the physical appearance of the phenotype it shows a gradient increase in the size from fittest to least fit. The smallest individual 1.08 is fittest while the largest 1.05 is the least fit.

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In generation 2 , the trend in similar that the largest (2.04) is least fit but the fittest is an interesting phenotype consisting of a group of 9 rotated flat discs (2.01).

Generation 3 also exhibits this trend very explicitly with the flattest phenotype (3.06) being the fittest and the largest one being the least fit (3.05).

Overall there is a some variation in morphology

Emergence Seminar Emergent Technologies & Design 2015-16

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SEQ 2 The second phase of our research continued with similar experiments done in sequence 1, involving geometric modifiers to produce iterations. 3 Generations were

Since this was essentially a digital experiment mimicking evolutionary processes, the exploratory methods deviate from nature. Also the breeding

produced with 10 individuals in each. 2 supplementary genes or geometric modifiers were added to the gene pool, making a total of 7 available genes. An additional fitness criteria was considered as an environmental vector. Mutation was thought of as a strategy to initially distribute the new genes and also to enhance or diminish the effect of desirable or undesirable genes in order to optimise the two fitness criteria. This strategy was possibly in contradiction to how mutation would work in nature

strategy involved three selected phenotypes from a generation; the fittest, the average fit and the least fit , all contributing to the genome of every offspring.

Moreover in multi parameter optimisation differential weightage was given to one criterion over the other.

Importantly, the concept of body plan is brought into the picture and the primitive(paraboloid) is divided into three parts. What this means is that each part of the genome would affect only certain parts of the body.

Emergence Seminar Emergent Technologies & Design 2015-16

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SEQ 2 4 G.

STRATEGY BODYPLAN

GENE POOL A B C D E F G

3 2 1

FITNESS CRITERIA

Copy 7.5 units (x-dir) Scale 1D = 3 times (z-dir local) Scale 3D = 0.5 Polar Array = 3 numbers XY plane(MP of edge) Rotate YZ plane 45 deg Array = 4 nos Z axis 1.5 unit dist Scale 1D=0.5 times X axis

fig. 29

BREEDING STRATEGY FITTEST AVERAGE LESS FIT

To increase surface Area to Volume

To minimize elevation area along Y-axis

fig. 30

KILLING STRATEGY

(3.6) (3.1) (3.5)

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

(3) (1) (1)

INTRODUCE NEW GENOME

F

(1)

OFFSPRING

E E A E A F

G

(6) fig. 31

Fittest, Average, Less fit are selected for breeding, the rest of the population is “Killed”

MUTATION STRATEGY Introduce gene F G into 100% (50% each) of the population 20% inversion

CROSSOVER STRATEGY FITTEST

(3)

E E A C A A

AVERAGE

(1)

E A D A D D

LESS FIT

(1)

A B B D E B

NEW GENES

(1)

F

(4.01) (4.02) (4.03) (4.04) (4.05) (4.06) (4.07) (4.08) (4.09) (4.10)

G

F/G 50% INSERTION 20% INVERSION

FINAL RESULT

1 2 3 4 5 6

1 2 3 4 5 6

1 2 3 4 5 6

E E A C A A E E A C

E E A C A

E E A C A

E A C A A E E A C A

A C A A E E A C A A

E A D A D D E A D A

A B B D E B A B B D

F G F G F G F G F G

E A C A

A E CA A F G A F A E

A

F

G

D A D

B B D E

G A E E F E E A E G AC A C F A C A A G

D

B

E A D

A B B

A

D

E A C A F G A F E E G A C G A

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

A G D A D D E A D A

F B B D E E A B B D fig. 32

24

Following the new fitness criteria (fig.30), the objective is to avoid the paraboloid’s enlargement on the X-Z plane but at the same time keep overall surface area, with low volume. Mutation frequency to begin with this Sequence is high since new genes ’F’ and ’G’ are inserted to the whole population in different parts of each genome, expecting to gain high levels of differentiation. At the same time, inversion inside 20% of the individuals is used to diverge similar genomes (fig.32).

Mean fitness value = 3.043 SD Factor = 0.466

SEQ 2 4 G.

Analysing the offspring, its observed that high frequency of genes ’A’ and ’F’ could be beneficial for the GENERATION 4: FITTEST AVERAGE AND LEAST FIT FROM G.3 fitness value as long as they multiply GEN. GENOME SURFACE VOLUME FIT1.

FIT2. MIN. FINAL FIT. RANK the primitive body parts, which as discussed earlier have a good ratio for (cm.) (cm.)

S/V ELEV AREA CRITERIA the first fitness criteria. Gene ‘B’ might not be helpful in Sequence 2 since it 4.01 E E A E A F 2308.28 1178.05 1.96 0.0032 3.25 3 tends to increase the X-Z elevation 4.02 E A C A G B 1563.62 1006.30 1.55 0.0046 2.91 7 area for each phenotype, therefore most of least fit individuals contain ’B’ 4.03 A C A F D B 2614.39 1718.12 1.52 0.0055 2.97 6 in their genomes. Genes ’G’ and ’C’ 4.04 C A G A A D 2025.72 1840.78 1.10 0.0055 2.48 10 (which used to have a neutral effect 4.05 A F A E D E 3831.15 2061.68 1.86 0.0045 3.26 2 in Sequence 1) reduce the Elevation 4.06 G A E B D E 1280.03 785.40 1.63 0.0056 3.10 5 area for X-Z plane (second fitness criteria), meaning they will be efficient 4.07 F E E A E A 2208.28 1178.10 1.87 0.0035 3.18 4 for the mean fitness value. Individual 1 4.08 E G A C A B 784.29 695.50 1.13 0.0066 2.62 8 presents ’G’ and ’C’ at the beginning 4.09 A C F A D B 1636.33 1461.60 1.12 0.0055 2.50 9 of body parts 1 and 2 respectively. the opposite, Gene ’E’ rotates the 4.10 G A A C A D 1097.00 655.31 1.67 0.0160 4.16 1 On body parts along X-axis and increases fig. 33 the X-Z elevation area affecting the second fitness criteria. Analysing the STANDARD DEVIATION FREQUENCY OF GENES difference between individual 1 and 2, it is observed that the presence of ’E’ fig. 34 fig. 35 contributed to reduce the fitness value by 12%, which is the maximum decrease in the population. Therefore reducing its frequency in the next generations could increase the mean fitness.

FITNESS VALUES

Additionally, the differentiation within the population is not as high as expected. The SD factor is 0.46 which is relatively a low value that makes the individuals cluster towards the mean fitness. Analysing the phenotypes it can be observed that indeed the overall proportion of the individuals is quite similar. Mutating 100% of the individuals with the same kind of mutation probably reduced the variation inside the population.

G1

PHENOTYPES

Individual Fitness value Genome

Individual Fitness value Genome

4.01 3.25

EEAEAF

4.02 2.91

EACAGB

th 4.06 (5 ) 3.10

GAEBDE

4.07 3.18

FEEAEA

4.03 2.97

ACAFDB

4.08 2.62

EGACAB

4.04 (10th) 2.48

CAGAAD

4.09 2.50

ACFADB

4.05 3.26

AFAEDE

ST 4.10 (1 ) 4.16

GAACAD fig. 36

Emergence Seminar Emergent Technologies & Design 2015-16

25

SEQ 2 5 G.

STRATEGY BODYPLAN

FITNESS CRITERIA

GENE POOL A B C D E F G

3 2 1

Copy 7.5 units (x-dir) Scale 1D = 3 times (z-dir local) Scale 3D = 0.5 Polar Array = 3 numbers XY plane(MP of edge) Rotate YZ plane 45 deg Array = 4 nos Z axis 1.5 unit dist Scale 1D=0.5 times X axis

fig. 37

BREEDING STRATEGY FITTEST AVERAGE LESS FIT OFFSPRING

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

(3) (2) (1)

G A A G A C

(6)

bodypart bodypart bodypart 1 2 3 fig. 39

CROSSOVER STRATEGY (3)

G A A C A D

AVERAGE

(2)

G A E B D E

LESS FIT

(1)

C A G A A D B

20% INSERTION 20% DELETION 1 2 3 4 5 6 (5.01) (5.02) (5.03) (5.04) (5.05) (5.06) (5.07) (5.08) (5.09) (5.10)

G A A C A D G A A C

A A C A D G A A C A

A C A D G A A C A D

G A E B D E G A E B

A E B D E G A E B D

C A G A A D B C A G

FINAL RESULT

1 2 3 4 5 6

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

A A C A E

D A D

C

1 2 3 4 5 6

A

G

G A G A D D G A A C

A A C D G G A A C A

A C A G A A A C A D

G A D D G E G A E B

A E B A D G A E B D

C A D A

D B C G A G fig. 40

26

To minimize elevation area along Y-axis

fig. 38

KILLING STRATEGY

(4.10) (4.06) (4.04)

FITTEST

To increase surface Area to Volume

Fittest, Average, Less fit are selected for breeding, the rest of the population is “Killed”

MUTATION STRATEGY 40% of the new population 20% Insertion 20% Deletion

As observed in Generation 4, the most desired genes will be probably be ’G’ and C; and the least will be E and B; while A and F depend on previous conditions in order to be efficient. At the same time, after the small amount of variation obtained in Generation 4, mutation strategy changes as Insertion and Deletion will both be applied to only 20% of the population each (fig.40).

This change will also affect the breeding strategy, as one more gene from the average individual (parent) will be selected for the offspring in replace of the extra genes added in the previous generation (fig.39).

GENERATION 5: FITTEST AVERAGE AND LEAST FIT FROM G.4 GEN. GENOME

5.01 5.02 5.03 5.04 5.05 5.06 5.07 5.08 5.09 5.10

GAAGAC AACAEA ACAEBG GCADBDA ADGDA DGAGD GAAGAB AACAECG ACAEBA CADBDG

SURFACE (cm.)

VOLUME (cm.)

FIT1.

S/V

FIT2. MIN. ELEV AREA

FINAL FIT. CRITERIA

RANK

718.33 392.50 959.55 453.27 538.73 390.40 1288.83 1210.11 842.84 506.43 794.59 517.32 888.71 649.19 583.03 264.13 708.35 514.82 832.48 573.52

1.83 2.12 1.38 1.07 1.66 1.54 1.37 2.21 1.38 1.45

0.0250 0.0093 0.0150 0.0080 0.0208 0.0223 0.0135 0.0093 0.0094 0.0094

4.17 3.26 3.04 2.25 3.72 3.71 2.92 3.35 2.63 2.69

1 5 6 10 2 3 7 4 9 8 fig. 41

STANDARD DEVIATION

FREQUENCY OF GENES fig. 42

Mean fitness value = 3.174 SD Factor = 0.555

fig. 43

Analysing the two charts above, it’s demonstrated that increasing both genes ‘G’ and ‘A’ improves the mean fitness value, which raised by 4% compared to Generation 4.

The most evident change though is the increase in SD factor by 19%, meaning variation improved when the percentage of individuals mutated reduced. Also, gene ‘G’ was prioritised in mutation Insertion, therefore gene ‘F’ was left apart, since its benefit to a genome is conditioned by previous operations. It is also observed that gene ‘B’ frequency in the population was reduced, probably reinforcing the idea about the disadvantage of including this gene. Using Deletion mutation for gene ‘E’ to 20% of the population was apparently also beneficial for the overall mean fitness value, since the 3 fittest individuals show no presence of ‘E’ in their genomes, being the second and the third the mutated individuals (fig.44).

SEQ 2 5 G.

FITNESS VALUES

G4 G5

PHENOTYPES

Individual Fitness value Genome

Individual Fitness value Genome

5.01 (1ST) 4.17

5.02 (5th) 3.26

5.03 3.04

GAAGAC

AACAEA

ACAEBG

5.06 3.71

5.07 2.92

5.08 3.35

DGAGD

GAAGAB

AACAECG

5.04 (10th) 2.25

GCADBDA

5.09 2.63

ACAEBA

5.05 3.72

ADGDA

5.10 2.69

CADBDG fig. 44

7

Emergence Seminar Emergent Technologies & Design 2015-16

27

SEQ 2 6 G.

STRATEGY GENE POOL

BODYPLAN

A B C D E F G

3 2 1

FITNESS CRITERIA

Copy 7.5 units (x-dir) Scale 1D = 3 times (z-dir local) Scale 3D = 0.5 Polar Array = 3 numbers XY plane(MP of edge) Rotate YZ plane 45 deg Array = 4 nos Z axis 1.5 unit dist Scale 1D=0.5 times X axis

fig. 45

(5.01) (5.02) (5.04)

OFFSPRING

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

(3) (2) (1)

G A A A A G

(6) fig. 47

CROSSOVER STRATEGY FITTEST

(3)

G A A G A C

AVERAGE

(1)

A A C A E A

LESS FIT

(1)

G C A D B D A

20% INSERTION 20% DELETION 1 2 3 4 5 6 (6.01) (6.02) (6.03) (6.04) (6.05) (6.06) (6.07) (6.08) (6.09) (6.10)

G A A G A C G A A G

A A G A C G A A G A

A G A C G A A G A C

A A C A E A A A C A

A C A E A A A C A E

G C A D B D A G C A

FINAL RESULT

1 2 3 4 5 6

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

D A A G A

A D B

C

1 2 3 4 5 6

A

G

G A A G A C G D A G

A A D A C G A A G A

A G G C G A A A A C

A A A A A A A G C A

A C C D B A A A A E

G C A A

D A C G C A fig. 48

28

To minimize elevation area along Y-axis

fig. 46

KILLING STRATEGY

BREEDING STRATEGY FITTEST AVERAGE LESS FIT

To increase surface Area to Volume

Fittest, Average, Less fit are selected for breeding, the rest of the population is “Killed”

MUTATION STRATEGY 40% of the new population 20% Insertion 20% Deletion

5th generation’s strategies are almost the same for breeding the 6th generation, since it brought improvement for mean fitness values and variation. The only change in the strategy is within Insertion as the selected gene to be inserted is D. This gene, as pointed before, can increase the first fitness criteria without affecting much the second as long as the body parts remain flat, with low volume and low elevation area. Moreover, it can be predicted that gene E frequency will continue to drop as only one of the parents (individual 5.2) contains the gene in its genome.

Even after the Deletion mutation, it can be eradicated from the entire population. At the same time A frequency is expected to keep rising since its present more than once inside each parent’s genome.

GENERATION 6: FITTEST AVERAGE AND LEAST FIT FROM G.5 GEN. GENOME 6.01 6.02 6.03 6.04 6.05 6.06 6.07 6.08 6.09 6.10

GAAAAG AAGACC ADGACAA GACAED ACGEAB CGAAAD GAAAAA DAAGACG AGACAC GACAEA

SURFACE (cm.)

VOLUME (cm.)

FIT1.

S/V

FIT2. MIN. ELEV AREA

FINAL FIT. CRITERIA

RANK

5600.28 2402.69 514.06 191.61 6813.22 535.82 2402.69 2861.35 766.45 329.24

4684.64 7725.30 426.46 147.26 29390.30 189.47 7725.19 6010.04 1178.09 441.78

1.81 1.63 1.62 1.55 1.73 0.80 0.72 0.80 1.67 1.17

0.0168 0.0418 0.0169 0.0166 0.0146 0.0152 0.0168 0.0145 0.0317 0.0095

3.04 4.47 3.23 3.28 2.45 3.16 2.85 2.68 3.95 3.25

7 1 5 3 10 6 8 9 2 4 fig. 49

STANDARD DEVIATION

FREQUENCY OF GENES fig. 50

Mean fitness value = 3.236 SD Factor = 0.562

fig. 51

Comparing the graphs above, it can be seen that the population’s variation did not change much, only increased by 1.26 % probably due to high frequencies of gene ‘A’ in all the population. The Mean Fitness Value increased only by 1.95 %.

Looking at the phenotypes it can be observed that most of the individuals got flattened, hence the first fitness criteria is satisfied for the population, increasing by 9% compared to previous generation. Nonetheless, the second fitness criteria which has more weight for the overall fitness, is reduced by 4.5 % in this generation, and this can also be observed from the phenotypes as their shapes god wider in X-Z plane elevation.

SEQ 2 6 G.

FITNESS VALUES

G5 G6

PHENOTYPES

Individual Fitness value Genome

Individual Fitness value Genome

6.01 3.04

6.03 (5th) 3.23

GAAAAG

AAGACC

ADGACAA

GACAD

ACGAB

6.06 3.16

6.07 2.85

6.08 2.68

6.09 3.95

6.10 3.25

GAAAAA

DAAGACG

AGACAC

GACAEA

CGAAAD

6.04 3.28

6.05 (10th) 2.45

6.02 (1ST) 4.47

fig. 52

Emergence Seminar Emergent Technologies & Design 2015-16

29

SEQ 2

STANDARD DEVIATION GRAPH COMPARISON As a conclusion, the graph shows how variation improved considerably (19%) from G4 to G5 mainly by reducing the amount of individuals mutated, although according to the Total Gene Frequency charts, it could be pointed that no further variation was achieved across the generations since gene A had high frequency in all genomes. Increasing gene G was indeed beneficial to the fitness criteria, as well as reducing E. However, higher values of C could have improved further the overall fitness since scaling down the overall shapes reduce considerably the X-Z elevation area without affecting the Surface Area/Volume ratio. Unfortunately this was not recognized during the experiments.

SD Values Actual values Mean fitness value SD Factor

G. 4 G. 4

G. 5 G. 5

G. 6 G. 6

3.043 0.466

3.174 0.555

3.236 0.562 fig. 53

STANDARD DEVIATION GRAPH COMPARISON

fig. 54

30

SEQ 2

G. 4 Individual Fitness value Genome

Individual Fitness value Genome

4.01 3.25

EEAEAF

4.02 2.91

EACAGB

th 4.06 (5 ) 3.10

GAEBDE

4.07 3.18

FEEAEA

4.03 2.97

ACAFDB

4.08 2.62

EGACAB

4.04 (10th) 2.48

CAGAAD

4.09 2.50

ACFADB

4.05 3.26

AFAEDE

ST 4.10 (1 ) 4.16

GAACAD

G. 5 Individual Fitness value Genome

Individual Fitness value Genome

5.01 (1ST) 4.17

5.02 (5th) 3.26

5.03 3.04

GAAGAC

AACAEA

ACAEBG

5.06 3.71

5.07 2.92

5.08 3.35

DGAGD

GAAGAB

AACAECG

5.04 (10th) 2.25

GCADBDA

5.09 2.63

ACAEBA

5.05 3.72

ADGDA

5.10 2.69

CADBDG

G. 6 Individual Fitness value Genome

Individual Fitness value Genome

6.01 3.04

6.03 (5th) 3.23

GAAAAG

AAGACC

ADGACAA

GACAD

ACGAB

6.06 3.16

6.07 2.85

6.08 2.68

6.09 3.95

6.10 3.25

GAAAAA

DAAGACG

AGACAC

GACAEA

CGAAAD

6.04 3.28

6.05 (10th) 2.45

6.02 (1ST) 4.47

fig. 55

Emergence Seminar Emergent Technologies & Design 2015-16

31

SEQ 2

G. 4

Individual Fitness value Genome

ST 4.10 (1 ) 4.16

BAEADA

th 4.06 (5 ) 3.10

GAEBDE

4.04 (10th) 2.48

CAGAADB

G. 5 Individual Fitness value Genome

5.01 (1ST) 4.17

5.02 (5th) 3.26

5.04 (10th) 2.25

GAAGAC

AACAEA

GCADBDA

G. 6 Individual Fitness value Genome

6.01 (1ST) 4.47

6.03 (5th) 3.23

AAGACC

ADGACAA

6.05 (10th) 2.45

ACGAB fig. 56

In generation 4, the fitness criteria is, increase surface area to volume, according to ranking of fitness value, overall morphology shows quite some similarity. Ranking is seen to decrease with an increase in height. 4.10 fittest with a flatter appearance while the least fit (4.04), though having similar morphology , has more height.

In generation 5 and 6, to decrease elevation area on ZX plan is added as another fitness criteria, this will tend to be reduce the height and length in morphology of phenotypes. Hence, the phenotypes within generation 5 and 6, the configurations which are flatter, shorter will expect to be obtained higher fitness values.

32

The highest fitness values in each generation is G5.01 and G6.01 respectively, the observation from elevation analysis presents a flattened body and more complexity which benefit to increase surface. In contrast, G5.04 and G6.05 with a significant lower value are the least within 2 generation.

Emergence Seminar Emergent Technologies & Design 2015-16

33

34

SEQ 3 Based on the knowledge from the experiments in Our strategies are defined within the GA as percentage Sequences 1 and 2, focus shifted to the subject of urban values, for elitism, mutation and crossover. Their block morphologies. case study was and experiments meanings, biological analogies and relation to the were conducted on the Hutongs of Beijing,China . Lessons learnt from geometric manipulation and optimisation done within a 3D modelling(Rhino) environment needed to be transferred to more complex interlinked geometries of urban blocks and performed within an associative modelling platform(Grasshopper). It is done keeping in mind the key design aspects of the chosen urban block, the need for variation in morphology and also the software/hardware limitations. The fitness criteria are related to environmental and spatial facors, specifically minimising building surface exposure, maximising ground exposure, maximising volume. The latter is in conflict with the other objectives. The multi objective optimisation search was done with the help of a genetic algorithm(GA) plugin (Octopus) linked to Grasshopper.

Emergence Seminar Emergent Technologies & Design 2015-16

hutong is described in the following sections. . 10 Generations were computed with a population of 10 in each, stopping after every third generation to analyse the normal distribution curves.

35

SEQ 3

GA <> BIOLOGICAL ANALOGY Genetic algorithms simulate the survival of the most optimal, not necessarily the fittest, over consecutive generations.

Each generation consists of a population of character strings that are analogous to the chromosome we see in DNA. Thus a chromosome (solution) is composed of several genes (variables) Each individual presents a point in search space and a possible solution. A fitness score is assigned to each solution representing the abilities of an individual to compete in each criteria. The individual with the optimal (or generally near optimal) fitness score is sought. The GA aims to use selective breeding of the solutions to generate offsprings better than the parents by combining information from their genomes.

New generations are produced containing on average more desirable genes than a typical solution in a previous generation.

In this way it is hoped that fitter individuals would thrive and the less fit would die out. Eventually, with successive generations the population would converge and not produce offspring noticeably different, then the algorithm itself is said to have converged.

Pareto optimality, is a state in which it is impossible to make any one individual better off without making at least one individual worse off. In multi objective optimisation, it would mean that its impossible to make a non-dominant individual better without compromising on the fitness value of one of the criteria. The simplest GAs use techniques like elitism(selection), crossover, mutation.

Elitism(selection): The algorithm selects chromosomes in the population for reproduction. The higher the rate, the more selective the algorithm gets in selecting the fitter individuals.

Crossover: This operation randomly points a locus on a sequence and exchanges the genes before and after that locus between two chromosomes to create offspring. The higher the rate, the more genes get passed onto the offspring from one parent chromosome.

Mutation: This operator randomly flips the sequence of some of the genes in a chromosome. The higher the rate, the more numbers of a population get mutated.

[M.Mitchell, 1996] [Doc.ic.ac.uk, 2016] So in theory, if the GA strategy is concentrated on selection, the population will be consisting mainly of similar fit individuals.

Using mainly selection and crossover would tend to cause the algorithm to converge on a good but sub-optimal solution.

Mutation can be unpredictable, as it just prompts a random walk through the search space.

HUTONGS, BEIJING,CHINA â&#x20AC;&#x201C; 15TH CENTURY The hutongs are the ancient alleyways or lanes formed in the negative spaces of rows of courtyard houses. Some hutongs are aligned, while some are staggered and constantly turning corners. There are hutongs that are as wide as 32 meters or as narrow as 40 centimeters. What gives the hutongs their unique character are the courtyard houses , hiding behind their long attractive curtain walls. The main buildings in the hutongs are almost always quadrangles. The courtyards are one of the most interesting features of traditional Chinese architecture.

The quadrangles in Beijing generally face south for better lighting, and so a lot of hutongs run east to west. Between the big hutongs many smaller ones run north to south for convenient passage. These can be considered secondary alleyways. The height of the gates or the front street facing walls are also interesting indicators of what lies behind them.

[K.Ray, 2001]

36

fig. 57

An associative model is defined for the primitive block, a cluster of four courtyard houses of the Hutongs, in order to be fed into the GA. The algorithm in turn manipulates the various distinct elements of the block and evaluates the fitness values for every iteration. It means the script would run as many times as the number of populations, hence computational time is a factor in this workflow. But due to the relatively simple geometric parts of the hutong , this wasnâ&#x20AC;&#x2122;t so critical .The definition was compiled in grasshopper according to the pseudocode below.

The script is started by defining a fixed plot size for a single courtyard house. Separate genes for the building height, width, depth, street width are defined. The plot is further divided into the various segments of the traditional house plan. Such that each gene could act differently on the body parts. The single house plan is then copied to make 2 adjacent houses. They are then mirrored from the midpoint of the street width to get 4 house plans. The height gene(1 or 2 storeys) act on the various parts of the 4 houses in a discrete manner.

SEQ 3

BLOCK DEFINITION

PSEUDOCODE - Define a fixed plot size for single courtyard house - Offset the longer sides according to the primary building depth gene - Get intersection points between the offset lines and the shorter sides - Split the shorter side into segments based on intersection points - Further split the shorter side on the south into 3 parts for the entrance blocks - Offset the shorter sides as per the primary or secondary building depth gene - Loft original edges and the offset edges to get separate surfaces

- Copy the entire geometry to be placed adjacent to the long side - Reconnect the plan dimensions to a separate set of genes.

- Define the street width - Mirror the 2 adjacent house plans with the mirror plane being the midpoint of the street width - Reconnect the mirrored houseplan dimensions to a separate set of genes.

- Set a conditional statement that when the street width exceeds a limit the height genes for the entrance blocks are maximised - Extrude all the lofted surfaces with discrete height genes

5

2

6 4

N 3

1. Create a plot for single courtyard house (28x20m)

2. Offset longer edges and split the shorter edges

1

3. Offset shorter sides according to primary/secondary building depth gene

idth

etw stre

4. Copy

5. Define street width and mirror 5a. Loft the ofset edges and the original edges

Emergence Seminar Emergent Technologies & Design 2015-16

6. Extrude the lofted surfaces with seperate values

fig. 58

37

SEQ 3

GROWTH STRATEGY

SUPERBLOCK

An associative model is set up with the various zones or elements of the hutong courtyard house linked to different geometric modifiers. It is akin to the body parts of a phenotype being acted on by genes. Sometimes multiple parts are affected by the same gene. Each geometric modifier or gene has a range defined within. It is basically the intensity with which it modifies the body part. In addition to the hox gene effect, of directing the modifiers on certain body parts, there was also the use of a conditional statement which is analogous to a genetic switch.

Basically, when the street width exceeds a certain limit, the switch triggers an activator which maximises the height of the street facing block(body part 5) [S. Carroll, 2005]

6

BODYPLAN

3

fig. 59

2 4

1

5

X

GENE POOL

5

Y

fig. 60

Z

BLOCK HEIGHT 1 2 3

5

PRIMARY BLOCK 1 DEPTH 2

6

3

4

3 -12 m

3 -8 m

fig. 61

SECONDARY BLOCK DEPTH 4

fig. 62

3 -5 m

STREET WIDTH// HEIGHT

5

fig. 63

6 -15 m 3 -5 m

6

fig. 64

FITNESS CRITERIA MINIMIZE EXPOSURE OF THE BUILDING BLOCKS

MAXIMIZE EXPOSURE OF THE GROUND LEVEL

fig. 65 38

MAXIMIZE VOLUME

fig. 66

fig. 67

0.5 0.1 0.5 0.8

In this generation, it is seen from the polygonal graphs that phenotype 3.2 is the pareto optimal,having almost balanced values for all objectives. While the least fit phenotype 3.6 has miaximum values for volume but this has drastically affected the other 2 criteria.

As far as the normal distribution curve is concerned, the shape suggests slightly less variation in the population; and the vast majority of the population is clustered near the mean value. Although, more phenotypes have fitness values exceeding the mean.

SEQ 3 3 G.

Elitism Rate Mutation Probability Mutation Rate Crossover Rate

PHENOTYPES Maximize volume

Maximize ground exposure

3.1

Minimize building exposure

NO. Volume Min. Surface Exposure Max. Building Exposure Final Fitness

1 1.31 1.58 1.46 4.31

2 (1ST) 1.65 1.46 1.48 5.25

3.6

NO. Volume Min. Surface Exposure Max. ground Exposure Final Fitness

3.3

3.2

6 (10th) 1.98 1.00 1.00 4.05

3 1.37 1.58 2.00 5.00

4 1.00 2.00 1.00 4.67

3.8

3.7

7 1.27 1.67 1.33 4.68

3.5

3.4

5 1.73 1.22 1.36 4.79

3.9

8 1.83 1.33 1.56 5.00

4.0

9 (5th) 1.65 1.40 1.83 4.74

10 2.00 1.05 1.05 4.89 fig. 68

STANDARD DEVIATION 1.4

NORMAL DISTRIBUTION

1.2

G3

1 0.8 0.6

Mean fitness value = 4.74

0.4 0.2

SD Factor = 0.330

0 0

fig. 69

Emergence Seminar Emergent Technologies & Design 2015-16

1

2

3

4

5

STANDARD DEVIATION VALUES

6

7

fig. 70 39

SEQ 3 6 G.

Elitism Rate Mutation Probability Mutation Rate Crossover Rate

0.5 0.306 0.5 0.8

PHENOTYPES

The breeding strategy was slightly modified by increasing the mutation probability in the hope of increasing the variation in the population. The fittest phenotype has maximum fitness values for 2 criteria while having quite a low value in the other criterion. This is also seen in the second fittest(6.5).

But in this generation, the opposite trend is common, where one criteria is attempted to be maximised while compromising on the others.

These phenotypes end up having low overall fitness. As per our strategy, the ND graph showed an increase in variation but the mean fitness value decreased.

Maximize volume

6.2

6.1

Maximize ground exposure

6.4

6.3

6.5

Minimize building exposure

NO. Volume Min. Surface Exposure Max. ground Exposure Final Fitness

2 (10th) 1.04 1.50 1.03 3.56

1 (1ST) 2.00 1.10 2.00 5.10

3 1.46 1.00 1.59 4.05

6.7

6 1.22 1.92 1.00 4.14

7 1.48 1.07 1.54 4.09

5 1.79 1.31 1.77 4.87

6.9

6.8

6.6

NO. Volume Min. Surface Exposure Max. Building Exposure Final Fitness

4 1.83 1.12 1.31 4.26

6.10

8 1.43 1.67 1.27 4.36

9 1.49 1.75 1.01 4.25

10 (5th) 1.00 2.00 1.28 4.28 fig. 71

STANDARD DEVIATION 1.4

NORMAL DISTRIBUTION

1.2

G3 G6

1 0.8 0.6

Mean fitness value = 4.30 SD Factor = 0.407

0.4 0.2 0 0

1

2

3

4

5

6

7

STANDARD DEVIATION VALUES

fig. 72 40

fig. 73

0.5 0.6 0.5 0.8

The mutation probability was further increased to see the effects of changes to just one aspect of breeding. The fittest individual is somewhat balanced in terms of its fitness values.

The least fit phenotype again shows the trend of maximising one particular fitness value and compromising on the other two. The ND curve was expected to show further improvement in variation, but on the contrary the variation dropped quite drastically.

SEQ 3 10 G.

Elitism Rate Mutation Probability Mutation Rate Crossover Rate

PHENOTYPES Maximize volume

10.1

Maximize ground exposure

10.2

10.3

10.5

10.4

Minimize building exposure

NO. Volume Min. Surface Exposure Max. ground Exposure Final Fitness

1 (1ST) 1.38 1.81 1.58 4.77

10.6

NO. Volume Min. Surface Exposure Max. Building Exposure Final Fitness

6 2.00 1.00 1.35 4.35

2 1.81 1.62 1.00 4.44

3 1.65 1.26 1.32 4.23

4 1.06 1.56 1.97 4.60

10.9

10.8

10.7

7 (5th) 1.48 1.74 1.18 4.39

5 (10th) 1.06 2.00 1.15 4.21

8 1.00 1.60 2.00 4.60

10.10

9 1.75 1.59 1.01 4.34

10 1.36 1.92 1.20 4.48 fig. 74

STANDARD DEVIATION 3

NORMAL DISTRIBUTION

2.5

G6 G 10

2 1.5 1

Mean fitness value = 4.44 SD Factor = 0.167

0.5 0 0

1

2

3

4

5

6

STANDARD DEVIATION VALUES

fig. 75

Emergence Seminar Emergent Technologies & Design 2015-16

fig. 76 41

STANDARD DEVIATION GRAPH COMPARISON

Through this sequence, the breeding strategy was changed by modifying only the mutation probability and expecting predictable results. This seemed to have been a mistake as the ND curve initially showed more variation from generation 3 to 6, but variation drastically reduced from generation 6 to 10. This can possibly be attributed to the very high mutation probability, which basically makes an unpredictable tour of the search space.

Moreover the number of individuals that were fitter than the mean value decreased in successive generations. The only reason mean fitness improved from generation 6 to 10 was because of one very fit individual.

3

2.5

NORMAL DISTRIBUTION

SEQ 3

ANALYSIS

2

1.5

1

0.5

0 0

1

2

3

4

5

6

7

STANDARD DEVIATION VALUES

FITTEST INDIVIDUAL FINAL FITNESS Maximize volume

Maximize ground

3.02 5.25 3.2

Minimize building

SD Values Actual values

G. 3 G. 3

G. 6 G. 6

G. 10 G. 10

Mean fitness value SD Factor

4.738 0.330

4.296 0.406

4.441 0.167

6.1

6.01 5.10

Maximize volume

Maximize volume

Minimize building

Maximize ground

Maximize ground

fig. 77

10.01 4.77 10.1

Minimize building

LEAST FIT INDIVIDUAL FINAL FITNESS

3.06 4.05 Maximize volume

Maximize ground

3.6

Minimize building

6.2

6.02 3.56

Maximize volume

Maximize ground

Minimize building

Maximize volume

Maximize ground

10.5

10.02 4.21

Minimize building fig. 78

42

G. 3 NO. Final Fitness

ST 2 (1 ) 5.25

1 4.31

3.6

NO. Final Fitness

6.2

6.1

6.7

NO. Final Fitness

6 4.14

G. 10 NO. Final Fitness

10.1

1 (1ST) 4.77

6 4.35

4 (5th) 4.26

9 4.25

3 4.23

10 4.28

10.4

4 (5th) 4.60

10.9

10.8

8 4.60

5 4.87

6.10

10.3

10.7

7 4.39

6.5

6.9

10.2

10.6

NO. Final Fitness

3 4.05

8 4.36

2 (10th) 4.44

10 4.09

6.4

6.8

7 4.09

4.0

9 4.74

6.3

6.6

5 4.79

3.9

8 5.00

2 (10th) 3.56

1 (1ST) 5.10

4 4.67

3.8

7 (5th) 4.68

3.5

3.4

3 5.00

3.7

6 (10th) 4.05

G. 6 NO. Final Fitness

3.3

3.2

3.1

SEQ 3

PHENOTYPES

9 4.34

10.5

5 4.21

10.10

10 4.48 fig. 79

Emergence Seminar Emergent Technologies & Design 2015-16

43

SEQ 3

FITTEST & LEAST FIT

G. 3 NO. Final Fitness

G. 6 NO. Final Fitness

G. 10 NO. Final Fitness

3.2

ST 2 (1 ) 5.25

6.1

1 (1ST) 5.10

10.1

1 (1ST) 4.77

3.6

3.7

7 (5th) 4.68

6.2

2 (10th) 3.56

10.4

4 (5th) 4.60

6 (10th) 4.05

6.4

4 (5th) 4.26

10.2

2 (10th) 4.44 fig. 80

Through all the populations in this sequence, the variation in morphology is quite subtle. The limited domain of the genes in the definition is the reason for this. The only pareto optimal among these 3 generations is also the fittest. In the least fit individual, the values are more inclined towards the objective of minimising building exposure.

44

Emergence Seminar Emergent Technologies & Design 2015-16

45

46

SEQ 4 In this phase of the research, the superblock is constituted of a cluster of 16 numbers of the singular courtyard house. Taking forward the observations from previous experiments and the knowledge of using the GA plugin in sequence 3, the objective in this sequence was to generate iterations that could expand the morphological possibilities at the superblock level in the hope of increasing the potential of finding an optimal solution. The fitness criteria was set up such that, the condition for maximising the courtyard area would oppose the conditions for maximising building volume and maximising building exposure. In such a case it is almost impossible to guess the form of an optimal solution. Another objective at this stage was to create certain inter-relationships between the blocks that make up the super block. In the aim of creating morphological variations and inter-relationships between blocks, the uniquely chaotic features of the hutong presented opportunities. Some aspects that were missed out in the block definition in sequence3 like- Variation in street width with staggered placement of houses; randomised cross alleyways with varying widths; were added to the definition of the super block.

Emergence Seminar Emergent Technologies & Design 2015-16

Since the maximising of courtyards was a fitness criteria that was opposing two mutually aligning objectives, it was thought to be interesting to introduce a homeotic transformation. This would essentially morph an existing body part into something else by triggering of a homeotic gene. [S.Carroll, 2005] In this case, when the combined courtyard area of 2 adjacent blocks becomes less than a third of the maximum possible area, then this triggers the transformation of a large volume (body parts 1 and 2) into a thin slab and the area underneath is added to the area of the courtyards. It was hoped that this transformation would help in boosting the effect of the criterion concerning courtyard area on the GA. For the experiments, the evolution in the GA is stopped after every 30 generations, each generation having a population of 20 individuals.

47

SEQ 4

Changes in body parts 1-8 will affect directly the Fitness Criteria, while changes in parts 9 and 10 will mainly affect the volumes. Extreme values for block height and depth will possibly make big changes in the overall fitness values while the street and alley width will just affect some parts of the body plan hence not much the overall fitness value. The maximum courtyard area possible was found by minimizing the sizes of the body parts, and 1/3 of that value would be the threshold that triggers the homeotic gene mentioned previously.

SUPERBLOCK

GENE POOL

BODYPLAN

fig. 81

BLOCK HEIGHT 1 2

X

3 4 5

3 -12 m

2

3

Z 10

PRIMARY BLOCK 1 DEPTH

8

4

1

Y

7

6

5 9 fig. 82

2

6

3

7

7

3 -8 m

8

SECONDARY 4 STREET WIDTH// BLOCK DEPTH HEIGHT 3 -5 m

8

5

3 -5 m 15 m

6

5 6

ALLEY WIDTH 0 -5 m

9

10

**If a+b = 1/3 of max. courtyard area, then 1, 2 becomes a slab

b a fig. 83

fig. 84

fig. 85

fig. 86

fig. 87

FITNESS CRITERIA MINIMIZE EXPOSURE OF THE BUILDING BLOCKS

MAXIMIZE EXPOSURE OF THE GROUND LEVEL

fig. 88 48

MAXIMIZE VOLUME

fig. 89

fig. 90

The Crossover rate though is kept at a regular value (0.5) to avoid drops in the generations’ mean fitness value. The mutation probability and mutation rate values are kept reasonably high (0.5) in order to achieve diverged genomes.

The strategy before running the first 30 Generations in Octopus, are set in order to achieve high levels of variation within the populations. So the amount of fittest individuals to breed will be reduced.

So analysing the Normal Distribution curve it can be observed that SD factor is very low, meaning not much variation was obtained, hence the SD levels will be close to the mean and the curve will look compressed towards it.

PHENOTYPES

Moreover, it’s shown that most of the population’s fitness value is gathered close to the mean. And 4 individuals, 2 with high fitness value and 2 with low with 2 individuals that have clearly a higher fitness value and other 2 individuals with very low values compared to the overall population.

This 4 individuals are the ones that provide the small amount of differentiation. The least fit individuals fulfil the maximum courtyard exposure criterion and have low values for the other two criteria. While the fittest individuals perform exactly the opposite way (fig 91). The average individual satisfies max surface exposure the most, followed by volume and lastly the courtyard area.

SEQ 4 30 G.

0.30 0.50 0.50 0.50

Elitism Rate Mutation Probability Mutation Rate Crossover Rate

Surface

Courtyard

NO. Max. Max. Max. Final

NO. Max. Max. Max. Final

Volume

30.06

Volume Courtyard Bld. exposure Fitness

30.05

Volume Courtyard Bld. exposure Fitness

30.06 (1ST) 2.00 1.05 2.00 5.05

30.05 1.66 1.36 1.67 4.67

30.08

30.15

30.08 1.98 1.04 1.98 5.00

30.15 1.14 1.76 1.28 4.19

30.03

30.12

30.03 1.67 1.33 1.83 4.83

30.12 1.15 1.81 1.15 4.07

30.09

30.02

30.09 (10th) 1.53 1.46 1.60 4.59

30.02 (20th) 1.00 2.00 1.00 4.00

STANDARD DEVIATION

fig. 91

G6 G 10

Mean fitness value = 4.64 SD Factor = 0.292

fig. 92

Emergence Seminar Emergent Technologies & Design 2015-16

fig. 93 49

SEQ 4 62 G.

Elitism Rate Mutation Probability Mutation Rate Crossover Rate

0.60 0.50 0.50 0.60

PHENOTYPES

For the next 30 generations, Elitism and Crossover rate was increased to 0.6 so more fit individuals will be chosen to breed, and higher frequency of their genes will be present in the offspring genomes. Mutation probability and rate remain in 0.5 aiming for more differentiation.

Stopping the GA at Generation 62 showed not so different results from Generation 30. The Mean fitness value remained almost the same with a slight increase of 0.9%; and another negligible decrease of 0.7% in the SD factor.

This time a single individual seems to differentiate from the rest with the lowest fitness value, although the single fitness performance looks identical to its equivalent from generation 30. The remaining individuals are clustered in three groups, the larger being close to the mean. Average fitness individual for this generation satisfies both Surface and Courtyard Area criteria and at the same time keeps a descent value for Volume criterion.

Surface

Courtyard

NO. Max. Max. Max. Final

NO. Max. Max. Max. Final

Volume

62.12

Volume Courtyard Bld. exposure Fitness

62.10

Volume Courtyard Bld. exposure Fitness

62.12 (1ST) 1.92 1.17 1.98 5.07

62.10 1.45 1.60 1.65 4.71

62.15

62.13

62.15 2.00 1.00 2.00 5.00

62.13 1.18 1.80 1.26 4.24

62.03

62.04

62.03 1.94 1.99 1.05 4.98

62.04 1.11 1.17 1.94 4.22

62.11

62.14

62.11 (10th) 1.46 1.60 1.62 4.68

62.14 (20th) 1.00 2.00 1.00 4.00 fig. 94

STANDARD DEVIATION

G6 G 10

Mean fitness value = 4.68 SD Factor = 0.290

fig. 95 50

fig. 96

0.80 0.40 0.40 0.80

PHENOTYPES

The GA runs continuously up until generation 152 in the hope of seeing some improvement in fitness because of prolonged evolution, with the Elitism and Crossover rate increased to 0.8.

While mutation probability and rate is reduced to 0.4. The outcome though was as expected.

The Normal Distribution curve shows another small decrease in the Mean Fitness Value by 0.8 %, while the SD factor reduced considerably compared to previous outcome, by a 9 % from

Generation 62, leading to a narrower and taller curve shifted to the left. Clearly, this time only two individuals differ from the rest of the population, whose fitness values got even closer to the mean. Most of the average individuals are located on the right part of the curve, meaning that their fitness values are higher for the two similar fitness criteria.

SEQ 4 152 G.

Elitism Rate Mutation Probability Mutation Rate Crossover Rate

Surface

Courtyard

Volume

NO. Max. Max. Max. Final

Volume Courtyard Bld. exposure Fitness

NO. Max. Max. Max. Final

Volume Courtyard Bld. exposure Fitness

152.17 (1ST) 2.00 1.00 2.00 5.00

152.17

152.12 1.79 1.10 1.72 4.61

152.05

152.20 1.98 1.04 1.93 4.95

152.20

152.19 1.31 1.74 1.25 4.30

152.19

152.18

152.10

152.18 1.97 1.05 1.92 4.93

152.10 1.16 1.80 1.14 4.10

152.12

152.03

152.05 (10th) 1.64 1.44 1.60 4.67

152.03 (20th) 1.00 2.00 1.00 4.00 fig. 97

STANDARD DEVIATION

G6 G 10

Mean fitness value = 4.64 SD Factor = 0.260

fig. 98

Emergence Seminar Emergent Technologies & Design 2015-16

fig. 99 51

Comparing the Normal Distribution curves for the three generations, it’s observed that their population is similar in terms of fitness value. A higher differentiation could not be achieved since two of the fitness criteria, ‘Maximize Building Exposure’ and ‘Maximize Volume’, perform too well together. Therefore every time the GA finds good configurations for one of the criterion they will as well satisfy the other. This also has a direct effect on the phenotypes, since they might look similar.

Observing the polygonal graphs, it’s evident that the fittest individuals from each generation have really similar performance and their overall fitness value are almost identical (fig.102). The same effect occurs for the least fit and the average individuals from the three generations, although the average individual from generation 62 is the only one that to some extent satisfies more the Courtyard Area and Surface Exposure criteria. Therefore these individual is one of the few that breaks the strong bond between the Building criteria. Pareto optimal individual is always close to the average in this sequence, unlike in sequence 3 where it appeared as the fittest individual overall.

STANDARD DEVIATION GRAPH COMPARISON 1.60 1.40

NORMAL DISTRIBUTION

SEQ 4

ANALYSIS

1.20 1.00 0.80 0.60 0.40 0.20 0.00 3.00

3.50

4.00

4.50

5.00

5.50

6.00

FITNESS VALUE

AVERAGE INDIVIDUALS FROM THREE GENERATIONS SD Values

Actual values

G. 30 G. 30

G. 62 G. 62

Mean fitness value SD Factor

4.640 0.292

4.680 0.290

G. 152 G. 152 4.640 0.260 fig. 101

fig. 100

FITTEST INDIVIDUAL FINAL FITNESS

Surface

30.06 5.05

Volume

Courtyard

62.1230.12

30.06

Surface

Volume

Courtyard

152.17 152.17

5.07

Surface

5.00

Volume

Courtyard

LEAST FIT INDIVIDUAL FINAL FITNESS

Courtyard

Surface

30.02 30.02 4.00

Volume

Surface

Courtyard

30.14 62.14 4.00

Volume

152.03

Surface

Courtyard

152.03 4.00

Volume fig. 102

52

30.08

30.06

30.03

30.06 (1ST) 5.05

NO. Final Fitness

30.05

NO. Final Fitness

30.09

30.08 5.00

30.15

3.09 (10th) 4.59

30.03 4.83

30.02

30.12

30.05 4.67

SEQ 4

G. 30

30.15 4.19

3.02 (20th) 4.00

30.12 4.07

G. 62 62.03

62.15

62.12

62.12 (1ST) 5.07

NO. Final Fitness

62.10

62.15 5.00

62.03 4.98

62.10 4.71

62.13 4.24

62.11 (10th) 4.68

62.14

62.04

62.13

NO. Final Fitness

62.11

62.14 (20th) 4.00

62.04 4.22

G. 152 152.17

NO. Final Fitness

152.17 (1ST) 5.00

152.05

NO. Final Fitness

152.20

152.12 4.61

152.18

152.20 4.95

152.19

152.12

152.03

152.10

152.19 4.30

152.05 (10th) 4.67

152.18 4.93

152.10 4.10

152.03 (20th) 4.00 fig. 103

Emergence Seminar Emergent Technologies & Design 2015-16

53

SEQ 4

FITTEST & LEAST FIT

G. 30 32.5

NO. Final Fitness

30.09 (10th) 4.59

30.06 (1ST) 5.05

30.02 (20th) 4.00

G. 62 62.12

NO. Final Fitness

62.10

62.14

th 62.11 (10 ) 4.68

62.12 (1ST) 5.07

62.14 (20th) 4.00

G. 152 152.17

NO. Final Fitness

152.17 (1ST) 5.00

152.5 th 152.05 (10 ) 4.67

152.3

152.03 (20th) 4.00 fig. 104

Analysing the fittest individuals’ phenotypes (fig104) it can be observed that the superblocks configurations are very similar, and homeotic transformations (slabs replacing body parts) didn’t take place. On the other hand, the least fit phenotypes present many homeotic transformations and have the similar configurations. This clear difference between the categories of phenotypes (fittest and least) responds once again to the similar performance of two of the three criteria. The average individuals’ phenotypes show clearly a transition in between the previous two categories, with some homeotic transformations in the superblocks.

54

As a conclusion, more differentiation between the individuals would have been obtained by avoiding strong coupling between criteria. That way the GA will not show a case of grouping among phenotypes.

At the same time, arranging different genes in the script in order to build stronger relationships between the blocks would be beneficial in order to break the homogeneity present in the superblock configuration patterns.

SEQ 4

ANALYSIS PRIMITIVE BLOCK: Block:

Block Length: Bloc Width: Street Width:

Building Depth:

1-G1.1

Plot coverage: 169 m 62 m FAR: 15 m [max] 6 m [min] Height [storey]: 3-6 m

49.4% 0.99 1 [min] 2 [max]

Maximise Volume

1.63/ 2.00

Maximise Courtyard Area

1.00/ 2.00

Maximise Building exposure

1.93/ 2.00

fig. 105

Block:

Block Length: Bloc Width: Street Width:

Building Depth:

1-G30.6 178 m 65.4 m 10 m 3-8 m

Plot coverage: FAR: Height [storey]:

fig. 106

Block:

Block Length: Bloc Width: Street Width:

Building Depth:

2-G62.12 183 m 66 m 10 m 3-8 m

Plot coverage: FAR: Height [storey]:

fig. 107

Block:

Block Length: Bloc Width: Street Width:

Building Depth:

3-G62.15 181 m 65.8 m 10 m 3-8 m

Plot coverage: FAR: Height [storey]:

fig. 108

Block:

Block Length: Bloc Width: Street Width:

Building Depth: fig. 109

Emergence Seminar Emergent Technologies & Design 2015-16

4-G152.17 Plot coverage: 183 m 66 m 10 m 3-8 m

FAR: Height [storey]:

77.0% 1.75 1 [min] 4 [max]

80.0% 1.40 1 [min] 4 [max]

81% 1.38 1 [min] 4 [max]

81.0% 1.57 1 [min] 4 [max]

Maximise Volume

2.00/ 2.00

Maximise Courtyard Area

1.05/ 2.00

Maximise Building exposure

2.00/ 2.00

Maximise Volume

1.92/ 2.00

Maximise Courtyard Area

1.17/ 2.00

Maximise Building exposure

1.98/ 2.00

Maximise Volume

2.00/ 2.00

Maximise Courtyard Area

1.00/ 2.00

Maximise Building exposure

2.00/ 2.00

Maximise Volume

2.00/ 2.00

Maximise Courtyard Area

1.00/ 2.00

Maximise Building exposure

2.00/ 2.00

55

SEQ 4a 32 G.

Elitism Rate Mutation Probability Mutation Rate Crossover Rate

PHENOTYPES

0.30 0.50 0.50 0.50

This experiment was done to verify the observations made at the end of sequence 4, about the two fitness criteria aligning very well against the other criterion, which inturn causes the GA to form a linear distribution in the multi-objective graph. There were certain doubts as to whether there was a fault in setting up the definition of the associative model or the fact that the fitness criteria were not ideal. The fitness criteria was reduced to just 2, maximising surface exposure against maximising courtyard area. Populations of 20 inividuals were generated, stopping after every 30 generations to analyse.

NO. Min. Courtyard Max. Surface Final Fitness

32.07 (1ST) 1.43 1.85 3.28

32.06 1.58 1.67 3.25

32.02 1.61 1.61 3.22

32.18 (11th) 1.77 1.28 3.05

NO. Min. Surface Exposure Max. Building Exposure Final Fitness

32.20 1.38 1.67 3.05

32.04 1.68 1.13 2.81

32.13 1.68 1.11 2.79

32.11 (20th) 1.49 1.28 2.77 fig. 110

STANDARD DEVIATION 3.00

SD Values

NORMAL DISTRIBUTION

2.50

actual values 2.00

1.50

1.00

Mean fitness value = 3.12

0.50

0.00 0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

FITNESS VALUE

fig. 111

56

fig. 112

SD Factor = 0.1.06

0.60 0.75 0.75 0.50

PHENOTYPES

From the analysis of the multi-objective graph in the GA, it is seen that the distribution is more random than before and there is no obvious patterns being formed. Hence, the asssociative geometric definition is working. The position of phenotypes resting on one particular axis but farther away from the other axis means that the criteria are clearly in opposition . But the reading of the normal distribution curves show a low value for the standard deviation factor. Which means there is still less variation overall. This could be because the number of bodyparts contributing to the objective of maximising surface exposure is more than the parts influencing the courtyard area.

NO. Min. Courtyard Max. Surface Final Fitness

61.09 (1ST) 1.39 1.84 3.22

61.05 1.62 1.59 3.21

61.01 1.60 1.59 3.20

61.11 (11th) 1.48 1.67 3.14

NO. Min. Surface Exposure Max. Building Exposure Final Fitness

61.14 1.13 2.00 3.13

61.17 2.00 1.00 3.00

61.12 1.59 1.65 3.24

61.19 (20th) 1.17 1.72 2.89

SEQ 4a 61 G.

Elitism Rate Mutation Probability Mutation Rate Crossover Rate

fig. 113

STANDARD DEVIATION 3.00

SD Values

NORMAL DISTRIBUTION

2.50

actual values

2.00

1.50

Mean fitness value = 3.05 SD Factor = 0.158

1.00

0.50

0.00 0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

FITNESS VALUE

fig. 114

Emergence Seminar Emergent Technologies & Design 2015-16

fig. 115

57

FINAL CONCLUSION Mechanical operations and direct modelling tools used throughout the Sequences lead to understanding how Evolutionary processes in Biology are related to Evolutionary Computations in architectural and urban design. Looking back into nature has been a constant practice in the history of science; and architecture looks at the same idea by applying Evolutionary methods into the design process. Here critical strategies should be defined from an early stage. In urban planning, proposals are driven by external conditions like environment, internal spatial logics and maximum inter-relationships between them. Developing Generative Algorithms to achieve appropriate emergent solutions for diverse objectives relies on the effective setting up of the associative geometry, facilitating strategies and well defined evolutionary goals. Throughout the sequences it was noticed that defining the Criteria has a vital effect on the phenotypes outcome, for they could result clustered and standardized morphologies according to their response to criteria. For this reason, picturing in advance the optimum phenotypes result is directly linked to defining the algorithm genes that will shape the blocks.

Moving further into development of Superblocks, it is accurate to point out that thinking deeper on the relationship between the blocks inside the superblock is the first important step. Intelligent use of fitness criteria can create morphological relationship between various segments of a block as well as between blocks within a superblock and this could generate a wider array of phenotypes. Also, it would have been interesting to use the fitness criteria values as genetic switches to trigger certain genes. In Sequence 4, there was an approach on including homeotic genes that changed the blocksâ&#x20AC;&#x2122; shape when triggered by a specific criteria, but these relationships were not effective enough to offer much differentiation within the population. Therefore linking relationships with a well defined hierarchy of body parts might have stronger effects on the populationâ&#x20AC;&#x2122;s variation.

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REFERENCES -

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

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Mitchell, M. (1996). An introduction to genetic algorithms. Cambridge, Mass.: MIT Press.

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

-

Ray, K. (2001). China In Focus.

- Doc.ic.ac.uk,. (2016). Introduction to Genetic Algorithms. Retrieved 2 February 2016, from http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol1/hmw/article1.html -

Darwins-theory-of-evolution.com,. (2016). Darwinâ&#x20AC;&#x2122;s Theory Of Evolution. Retrieved 1 February 2016, from http://www.darwins-theory-of-evolution.com/

- Costa, J. (2009). The Darwinian Revelation: Tracing the Origin and Evolution of an Idea.

Bioscience, 59(10), 886-894. http://dx.doi.org/10.1525/bio.2009.59.10.10

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