EMERGENT TECHNOLOGIES & DESIGN 2015 | 2016 EMERGENCE WORKSHOP Yorgos Berdos . Marcella Carone . Francesco Massetti . Molly Minot
COURSE DIRECTOR MICHAEL WEINSTOCK GEORGE JERONIMIDIS STUDIO MASTER EVAN GREENBERG TUTORS ELIF ERDINE MANJA VAN DE WORP MOHAMMED MAKKI 2
CONTENTS 0. ABSTRACT 0. INTRODUCTION
04 05
1.0 SEQUENCE 01
07
2.0 SEQUENCE 02
10
3.0 SEQUENCE 03
15
4.0 SEQUENCE 04
22
5.0 CONCLUSION 6.0 BIBLIOGRAPHY
32 33
3
ABSTRACT The present research project demonstrates a series of experiments conducted inside the spectrum of Evolutionary Development. The use of evolutionary solvers and genetic algorithms in the design field has introduced many advantages in comparison with the traditional design methodologies. For instance, when it comes to complex urban environment research where multiple and often contradicting objectives have to be considered. The experiment was divided in four sequences. Initially, one solid primitive was selected, sphere in our case, to be modified according to a gene code, which in this case is a series of geometrical operations. The resulting forms are combined in order to produce new individuals. These individuals were evaluated according to a fitness criteria that represents an objective to be reached. In Sequence 02, a new conflicting fitness criteria was introduced, the crossover strategy changes according to the findings from Sequence 01 and a body plan was applied. For Sequence 03, we use the same digital tools to analyse an existing urban configuration, in particular the Manhattan Commissioners’ plan - 1811. Sequence 04 was based on the knowledge acquired from the previous sequences and the selected strategy corresponded to both real environmental and urban parameters. The common ground throughout the sequences is that this design approach does not aim to reach one single optimal solution that could operate in any environment but produces a range of solutions which are optimised in relation with a particular design environment and particular evolutionary goals. 4
INTRODUCTION As a synthetic field mediating the complexity of many negotiating agencies, architecture has for a very long time been bound to the notion of contingency. In mining the resources of computational design processes and genetic algorithms, architecture can open novel spaces of exploration and go far beyond any deterministic design proposal, which is produced under a single architectural concept and arbitrarily presents itself as the one optimal solution. “For the typologist, the type (eidos) is real and the variation an illusion, while for the populationist the type (average) is an abstraction and only the variation is real” 1 Ernst Mayr introduced the idea of population thinking in biology based on Darwin’s “On the Origin of Species by Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life”. However a similar conceptual division could be traced back in the “problem of universals” and on the way that form can been defined.The ancient greek concept of form can be represented by numerous words but mainly by eidos (εἶδος) and idea (ἰδέα). According to Plato, every object or quality in reality has a form and the phenomena are representations mimicking that form (shadows). The problem of how one general thing can be many things in particular was approached by considering that form is a distinct singular thing, which can cause plural representations of itself in particular objects. For instance, there are countless versions of chairs but the form of the “chairness” is one and its the essence of them all 2. The digital simulation of evolutionary processes is a well established technique for the study of biological systems, which has been increasingly applied into the field of 5
architecture. The real potential of this kind of computational techniques and genetic algorithms is to overcome the idea of typological thinking, which arises from the platonic view on form and introduce a design practice based on morphogenetic processes. These morphogenetic processes require an understanding of the architectural object as a population rather than a variation of types. To understand the population thinking concept, we have to understand the vitality of the differential relation between the individuals of a particular species. This relation can be represented by two sets of differences: the first one refers to the fact that each individual has to be different from the others but simultaneously the differences among individuals sustain the differences as a species. In population thinking a critical mass of different individuals is necessary in order to maintain a diversified genome and consequently a resilient species. In this context, mutation is the biological method that continuously replenishes the variability of the gene pool.
generation. The “intensiveness� can be traced in the way the various fitness criteria we set are forcing particular changes in our phenotypes by making several genes and sequences of genes more present and eliminating others. The topological thinking relates to the way we assigned the body plan in the various stages of our experiments. The key concepts of breeding-killing strategies, crossover - mutation rates and fitness criteria, arising from evolutionary developmental biology (Evo-Devo), in relation to the population, intensive and topological were applied to virtual three-dimensional objects and multiple variations of results were produced, which were every time evaluated according to a different evolutionary goal.
"First....the forms do not preexist the population, they are more like statistical results. The more a population assumes divergent forms, the more its multiplicity divides into multiplicities of a different nature....the more efficiently it distributes itself in the milieu, or divides up the milieu....Second, simultaneously and under the same conditions.... degrees are no longer measured in terms of increasing perfection....but in terms of differential relations and coefficients such as selection pressure, catalytic action, speed of propagation, rate of growth, evolution, mutation....Darwinism's two fundamental contributions move in the direction of a science of multiplicities: the substitution of populations for types, and the substitution of rates or differential relations for degrees." 3 1
6
For Deleuze, in opposition to the typological kind of thought, a multiplicity is based on the interrelations produced when accumulating individuals in relation to each other. Apart from the notion of multiplicity - the populational thinking -, according to Manuel Delanda the productive use of genetic algorithms in architecture necessitates the deployment of two more philosophical ideas that were present in Deleuze writing: the intensive and topological thinking. Populational, intensive and topological thinking can be considered as the basis for the evolutionary concept of the genesis of form. In our experiments, the populations we were dealing with were varying from ten to thirty individuals per
MAYR, Ernest. Computational Design Thinking. Variational Evolution. AD Reader, 2011
Aristotle in Metaphysics says that Plato devised the Forms to answer a weakness in the doctrine of Heraclitus, who held that nothing exists, but everything is in a state of flow. If nothing exists then nothing can be known. It is possible that Plato took the Socratic search for definitions and extrapolated it into a distinct metaphysical theory. Little is known of the historical Socrates’ own views, but the theory of Forms is likely a Platonic innovation. 2
DELEUZE, Gilles. GUATTARI, Felix. A Thousand Plateaus. University of Minnesota Press, Minneapolis, 1987. Page 48. 3
1.0 | SEQUENCE 01 GENE POOL
FITNESS CRITERIA
F=
A
A - scale 1.5
B
B- 30º
x
C
C- 50º
z
D
D- copy 50mm x
E
E- scale 1D x0.7 z
F
F- array polar (4,360º)
AREA VOLUME
maximum area and minimum volume moving away from the initial sphere
BREEDING STRATEGY strategy: increase fitness and achieve more variation on the genomes
2 generation th
G G- scale 1D x1.3 y
3th generation
2nd generation
CROSSOVER STRATEGY 50% | 50%
Fittest X Fittest and Fittest X Less Fit
3th generation
75% | 25%
*30% mutations
Fittest X Fittest and Fittest X Less Fit
INTRODUCTION Sequence 1 was the starting point to explore Genetic Algorithms concepts. In this stage, the research was developed without scripting or coding knowledges. The investigation’s aim was to manually simulate the process of evolutionary solvers to clearly understand both evolution and genetic theories. By manually setting parameters to genes, we were able to follow the transformation order and could singly analyse the relative geometrical outcomes. This process was important to comprehend all the biological concepts, its functions in the Evolution process and, mainly, how the Evolution Development can be wiser applied to Architecture. GENE POOL Since Sequence 1 was based on geometrical morphogenesis, the first step was to select one parent primitive and construct a gene pool from Rhino modifiers. For this experiment, we selected a 50mm radios sphere and a gene pool with seven main genes. Observation: for each centroid from the first used as a starting point genes transformations.
geometry, the sphere will be for the followed The sequence
of modifiers is call genome and the final shape is the result of this order. FITNESS CRITERIA To run the analysis a fitness criteria is requested and it is the landmark that will drive the process towards an evolutionary goal. Once we selected the sphere, our aim was to move away from the primitive shape, consequently we considered AREA over VOLUME an optimal fitness target. In other words, maximising the surface while minimising the volume. 1st GENERATION The generation 1 genomes were randomly created. The instructions coded in each of the genomes were applied to the primitive, giving form to ten individuals. The resultant phenotypes presents different geometries that can be considered as made of a variable amount of primitives and/or by slight modifications of them. We have to mention that since the order of the instructions are a sequence, it has a fundamental role in the morphogenetic processes. It is possible to identify several configurations in which transformations are defined at the genotypical level, but
7
COMPARISON FITNESS CRITERIA 1ST GENERATION Mean Fitness Value Standard Deviation factor
0.046 0.013
2ND GENERATION Mean Fitness Value Standard Deviation factor
0.053 0.009
3RD GENERATION Mean Fitness Value Standard Deviation factor
0.062 0.007
from each parent. We set a crossover strategy and defined how many genes from the two genomes will be selected and recombined. For this generation we defined point crossover, 50%/50%. For the breeding strategy a rule was set in order to increase the fitness value for the next generation. The six fittest individuals were bred between with each others, giving form to individuals that are, except for one pair of individuals, the fittest in Generation 2.
60
50
40
The four less fit individuals of Generation 1 were bred with the absolute fittest of the generation, giving form to the less fit individuals in Generation 2. It can be observed, in comparison with generation 1, that the overall fitness value was increased, while the variation factor decreased.
30
20
10
0
0
0.01 generation 1 SD
0.02
0.03 Generation 2 SD
0.04 Generation 3 SD
do not have an impact on the phenotype. This happened when genes as rotating or creating a polar array are applied to the primitive before not-uniform scaling or copying.
8
0.05
0.06 Generation 1
0.07 Generation 2
2nd GENERATION BREEDING AND STRATEGIES
0.08 Generation 3
0.09
3rd GENERATION MUTATION STRATEGIES
From Generation 2 to Generation 3 we CROSS OVER added a further level of complexity with the insertion of mutations and the possibility of breeding between generations. Generation 2 was created through breeding processes between individuals of The crossover strategy remained the Generation 1. From the ranking list, the six same used in the previous step and the fittest and the four less fit individuals were breeding strategy started to combine identified to breed and generate a third fit individuals from both generations. individual with a certain amount of genes Regarding Generation 2, individuals were
bred with a similar logic, in order to improve the overall fitness. The combinations were made within the five fittest individuals from Generation 2 and the three fittest individuals and the less fit from Generation 1. The first three offsprings in the fitness criteria ranking show that breeding fittest individuals in the same generation does not grant fit individuals. However, the combinations made across two generations illustrate that the fitter the parents are, the fitter the new individuals are. The process was made by using mutation strategies after the breeding process, in other words, first the parents genomes were combined and then the mutation was made. Inversion, duplication and deletion were applied to the genomes. The fittest individual of Generation 3 presented an inversion mutation, while the second fittest was made through a duplication, showing a genome with one additional gene. In addition, the fourth fittest had a deletion mutation and was coded by a genome of three genes. However, the deletion was also used to produce one of the less fit individuals in this generation. Analysing fitness distribution among the first three generations gave us a strategy knowledge to be applied in sequence
1ST GENERATION G 1.1
Mean Fitness Value 0.046
2ND GENERATION
E D C E
G 2.1
F C G B
G 2.2
F G D C
G 2.3
0.0768
G 1.2
G 1.4
B A C E
G 2.4
D G D B
G 2.5
B D D F
G 2.6
E G F A
G 2.7
D C B A
G 2.8
A D F G
G 2.9
G D A F 0.0302
E D C E
G 2.2
E D D B
G 1.1
E D C E
G 2.5
F G G B
E D D F
F G G B E D F G F C D B
E D B A
0.0411
0.0313
G 1.10
G 1.1
E D D C
0.0533
0.0355
G 1.9
E D D F
0.0541
0.0420
G 1.8
G 2.4 G 2.1
0.0556
0.0456
G 1.7
E D D B
0.0568
0.0461
G 1.6
E D C E
0.0596
0.0461
G 1.5
G 1.1 G 2.1
0.0596
0.0489
E D F A 0.0379
G 2.10
3rd GENERATION *
F C C E
0.0691
0.0556
G 1.3
Mean Fitness Value 0.053
E D A F 0.0379
F C C E
F C C E
G 2.1 F C C E G 2.5 F G G B G 2.4
E D D F
G 1.2
F C G B
G 2.3
E D D C
G 1.3
F G D C
G 1.2 F C G B G 2.5 F G G B G 1.10 G D A F G 2.1
F C C E
G 2.2 E D B D G 2.5 F G G B
Mean Fitness Value 0.062
E C D E
G 3.1
E D D D E
G 3.2
0.0768
0.0749
E D C B
G 3.3
E D C
G 3.4
0.0624
0.0624
F C C B
G 3.5
E D D B
G 3.6
E D D C
G 3.7
0.0599
0.0596
0.0596
F C G B
G 3.8
G D E
G 3.9
E D B F
G 3.10
0.0556
0.0584
0.0530
9
2.0 | SEQUENCE 02 FITNESS CRITERIA
CROSSOVER STRATEGY
2nd to 4th generations AREA VOLUME
4th generation
F1=
75% | 50%
F2= SOLAR EXPOSURE AREA
F= F1 + F2 2 maximum surface exposed to sun, with less volume
10
60% | 75% 5th generation
AREA VOLUME
*20% mutations
67% | 60% 6th generation
F1=
increase genome length
5th to 6th generations
*40% mutations
4th GENERATION - MULTIPLE FITNESS CRITERIA Once we realised that the gene sequence is the key to create a fit individual, our aim in this sequence was to increase the genome length in order to achieve better results thought generations.
addition, we noticed that overall variations decreased and some genes (A and B) were lost during the process. 5th GENERATION
In Generation 5, the breeding strategy does not only focus on fittest individuals (the two fittest from each generation from We proposed an innovative crossover 1 to 3 and the five fittest from Generation strategy that consisted in a one point 4), but also select from Generation 1 four crossover 75% 50% that select the first random less fit individuals. The aim of this three genes from the fittest individual approach was to reintroduce some genes and the first two of the other individual, that were previously lost, once they were generating a five genes genome. not dominant genes held by individuals with lower fitness. For the breeding method, once we realised that breed fittest individuals from The crossover strategy followed the past generation was advantageous, our same logic to extend the genome and we strategy remained the same. However, designated one point crossover 60% 75%, once we introduced a new fitness criteria, increasing the genome length from five to we evaluated the phenotypes of the fittest six genes. individuals from Generation 1, 2 and 3 in a new rank. (The new fitness criteria In addition, Generation 5 added mutations was the average between the two values and the notion of body plan. Firstly, from maximising area over volume and regarding mutations, some genomes maximising total solar exposure over area, suffered alteration by inaccuracies in the in other words, we tried to achieve the information transcription process, usually maximum surface exposed to sun, with leading to deletion, inversion, insertion or less volume). duplication of one or more genes within a genome, changing the order in which Comparisons could be made within instructions are coded. generations and evaluated according the same number and type of criteria. In Secondly, regarding the body plan,
BREEDING STRATEGY 4th generation
Fittest X Fittest
strategy: increase fitness
5th generation
we specified what genes (geometrical transformations) were expressed in different parts of the individual phenotype. The primitive sphere was divided into three parts oriented towards a vector, representing the solar exposure direction.
Less Variation on the genomes Lost genes A and B
Fittest X Fittest and Fittest X Random Less Fit
control population variation.
The Body plan application visibly underline the complexity process, once have the ability of strongly modify the phenotype of individuals. In addition, the number of subdivision in the plan and specific genes When genomes are six genes long, the assignment had a highly influence in the first two genes were applied to the first offsprings. body plan sector and the next two to the second part. In the case of seven genes genome, the three central ones were coded to the second sector of the body plan. As a result, the mean fitness value increased as well as variation. 6th GENERATION
strategy: more variation on the genomes
6th generation
Fittest X Fittest and Fittest X Random Less Fit
strategy: more variation on the genomes
The sixth generation was obtained by increasing to 40% the mutation and applying to a one point 67% 60% crossover, generating genomes with seven genes (the first four from the fittest individual and the first three from the other individual). For the breeding strategy, we selected the fittest individual of each previous generation and the five fittest individuals from Generation 5. As done before to increase variation, two less fit individuals were included into the process. Comparing normal distribution of fitness criteria, between Generation 5 and Generation 6, it is clear that introduce mutations had a fundamental role in 11
COMPARISON FITNESS CRITERIA 1
BODY PLAN
6
from 4th generation NORMAL DISTRIBUTION
5
sun vector
30ยบ
re su
la so
o xp e r
4
3
2
1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
FITNESS VALUE
1 2
generation 1 SD
Generation 2 SD
Generation 3 SD
Generation 4 SD
Generation 1
Generation 2
Generation 3
Generation 4
COMPARISON FITNESS CRITERIA 2 7
3
6
Normal Distribution
5
Genome acting to a specific body plan sector
4
3
2
1
1 2
6 genes genome
0
0.2
0.4
0.6
0.8
1
1.2
Fitness Value
2 3
12
0
1
Generation 4 SD
3
7 genes genome
Generation 5 SD
Generation 6 SD
Generation 4
4th generation Mean Fitness Value Standard Deviation factor
0.537 0.064
5th generation Mean Fitness Value Standard Deviation factor
0.694 0.137
Generation 5
Generation 6
6th generation Mean Fitness Value Standard Deviation factor
0.816 0.093
4TH GENERATION G 4.1
Mean Fitness Value 0.537
5TH GENERATION *
E D D E D
G 5.1
E D C E D
G 5.2
E D C E D
G 5.3
E C D F C
G 5.4
E C D F C
G 5.5
E D C F C
G 5.6
E C D F G
G 5.7
E D C F G
G 5.8
E D D F G
G 5.9
F D D F G
G 5.10
0.723 0.623 0.673
G 4.2
0.428 0.450 0.439
E C D D C B
G 6.4
E D D B A C
G 6.5
E D D E C D
G 6.6
E C D F C G
G 6.7
E D D E C D
G 6.8
E D D A D F
G 6.9
E D C B A C
G 6.10
D C E D E E C E D D E C C
1.10 0.630 0.865
E D D B E D D
0.943 0.746 0.8445
E C D D E D D
0.951 0.712 0.832
E D D B E C D
0.973 0.670 0.822
E D D B E D C
0.957 0.667 0.812
0.563 0.586 0.575
0.225 0.632 0.429
E D D E E D D
1.140 0.625 0.883
0.660 0.503 0.581
0.522 0.455 0.489
G 4.10
G 6.3
0.675 0.514 0.595
0.541 0.455 0.498
G 4.9
E D B D D D
E D D E E C D
1.030 0.743 0.887
0.844 0.599 0.722
0.541 0.455 0.498
G 4.8
G 6.2
0.807 0.659 0.733
0.569 0.486 0.528
G 4.7
E C E D D D
Mean Fitness Value 0.816 1.150 0.674 0.912
0.885 0.659 0.772
0.569 0.486 0.528
G 4.6
G 6.1
0.870 0.735 0.803
0.569 0.486 0.528
G 4.5
E D D E D C
0.995 0.719 0.857
0.702 0.491 0.597
G 4.4
6TH GENERATION *
1.070 0.689 0.879
0.702 0.491 0.597
G 4.3
Mean Fitness Value 0.694
E D D E D G D
0.799 0.655 0.727
E C D D F C C
0.675 0.483 0.579
13
G 4.10 G 4.9 G 5.9
G 4.8 G 4.7 G 4.6 G 4.5
G 4.2 G 4.1
G 5.3 G 5.2
G 5.1
G 6.8 G 6.7
G 6.6 G 6.5
G 6.4
FITTEST
4TH GENERATION
5TH GENERATION
6TH GENERATION 14
G 6.10 G 6.9
G 6.3 G 6.2
G 6.1
G 5.6
G 5.4
G 4.3
G 5.8 G 5.7
G 5.5
G 4.4
G 5.10
LESS FIT
3.0 | SEQUENCE 03 INTRODUCTION
[MANHATTAN COMMISSIONERS’ PLAN - 1811] 1
2
3
4
2
4
After a critical revision of Sequences 01 and 02, concerning breeding and killing strategies and the reached results, we could translate this knowledge to a digital process in Octopus Plugin. In this phase, we increased the complexity by analysing Urban Blocks evolution thought Evolutionary Computation tools and by combing various contradictory fitness criteria. We run a variety of digital experiments with a superblock, composed by four blocks each (2x2). Manhattan Commissioner’s Plan was selected to be the initial point and we decided to comprehend and follow the real proposal premises for a precise definition of the body plan and the development strategies. THE GREATEST GRID
1
3
absorb the rapid American urban growth between 1810 and 1860 by designing a regular, predictable grid. Characterized by the uniformity, Manhattan’s plan proposed a gridiron street system, which proposed 12 main avenues running North to South and 155 Streets running West to East. The result is a series of horizontal blocks, all with the same dimensions, creating the stereotypical illustration of New York City. By 1945, New York already exhibited its high-density characteristics. The buildings’ high are guided by the grid and its relations between streets and avenuesthe tallest constructions are located in the block boundary facing the avenues and, in contrast, the small buildings are located along the longer boundary of the block, facing the streets. (in the inner space).
“With that simple action they describe a city of 13 x 156 = 2,028 blocks (excluding topographical accidents): a matrix that captures, at the same time, all remaining territory and all future activity on the island. The Manhattan Grid.” 3 Presented as a clearly top-down urban planning approach, Manhattan Commissioner’s Plan 1811 was created to
15
PSEUDOCODE
PSEUDOCODE TO DESIGN THE BLOCK STRATEGY
[8] extrude higher buldings
[7] extrude lower buldings
[6] surfaces
[5] random offset in each plot
[4] divide the longer edges and connect the points
[3] divide the two curves and connect the points
[2] offset the shorter edges
[1] 200m x 60m
16
Digitally generate the well-known New York orthogonal grid was the starting point to develop a correct analysis. The first step was to divide the primitive block into smaller terrains, facing either the main avenues or streets. Our aim was to trail “Manhattanism” and to produce superblock variations that follow the regulation of higher buildings, 6 to 20 floors, when fronting avenues and smaller buildings, 0 to 6 floors, when fronting streets.
In order to understand Octopus operational logic, in this sequence we run a series of experiments based on sequence 01 and 02 analysis. Firstly, regarding mutation probability, we decided to start with 0.00 probability and increase the value thought generations to see the percentage of variation caused in each result.
For the Elitism criteria, we started with In addition, the definition was written a 0.50 value for Generations 3 and 6 to predict voids in the middle of the and decreased it to 0.20 for the 10th block, offering a different parameter in Generation. For the Crossover Rate, we comparison with the real Manhattan. gradually increased the value from 0.50 to 0.80. BODY PLAN AND GENES To achieve a reasonable result, we run a The phenotypical diversity was created series of 10 Generation, with 10 individuals by mapping the superblock geography each. and by designing the body plan scheme. The primitive block was divided in two FITNESS CRITERIA main parts, higher and lower buildings, defining the “switches” for each gene and With the Octopus application, we could specifically controlling each parameter. combine three conflicting fitness criteria. Our goal was to maximize buildings’ Six genes that have different roles, volume while minimizing exposed surfaces regarding distances and body plan area and ground area. For this sequence, actuation, compose the gene pool for this we did not integrate a sun vector; the sequence. For instance, the diversity in exposed area is related to the surface area phenotypes is controlled by modifiers that of the buildings. regulate building heights, offset from the streets and inner courtyard width.
BODY PLAN
3rd GENERATION In this generation, by analysing the convergence graph, it is clear that just one of the fitness criteria, the ground level area, had some alteration. The fitness criteria and standard deviation graph shows that we did not have much variation between the individuals. Visually, the phenotypes presented similar characteristics.
[1] [1]
6th GENERATION
[2]
ground floor exposed
block
FITNESS CRITERIA F1= Maximize the volume size [Buildings Volume] F2= Minimize total exposure of the building blocks [Exposed surface] F3= Maximize the ground level exposure [Block Area - Buildings Area]
F= F1 + F2 + F3 3
that has a slightly lower fitness value, the phenotypes in all generations did not exhibit evident differences between each other. We assumed that our gene pool domain was quite limited and could not lead to great phenotypical diversity and, consequently, a visually noticeable gradual evolution. In sequence 04, we reviewed these parameters in order to reach better results.
In comparison with the 3rd generation, we got more fit in this stage. However, the raise is minimum regarding both average and the fittest individuals’ values. As in the previous generations, just the ground level area criteria seems to have a slight variance. 10th GENERATION Regarding the increase of the exploitation in this generation and in order to achieve a bigger variance in the fitness value, we reduced the Elitism value to 0.20. However, this decision caused the reduction of the average Fitness Value for this generation. Analysing the convergence graphs allowed us to comprehend that. OBSERVATIONS Evaluating the overall achievements in this experiment, besides our 10th generation
KOOLHAAS, Rem. Delirious New York. Oxford University Press, 1978. pg.18 3
17
GENE POOL A - Floors 6 to 20 [1] B - Floors 0 to 6 [2] C - Offset 0.0 to 10.0m [1] D - Offset 0.0 to 5.0m [2] E - Offset 18.0 to 30.5m [2]
200m
10 GENERATIONS 10 INDIVIDUALS PER GENERATION
6
7
8
9
10
11
12
21
2
22
3
23
4
30m 18
5
13
17.50m
14
15
16
17
18
19
20
24
60m
15.25m
1
3RD GENERATION G 3.1
Fitness Value 2.436
G 3.5
Fitness Value 2.556
G 3.9
Fitness Value 2.947
Strategy 0.50 0.00 0.50 0.50 10
Elitism Mutation Probability Mutation Rate Crossover Rate Population Size Fitness Criteria
G 3.2
Fitness Value 2.445
G 3.6
Fitness Value 2.629
G 3.10
Fitness Value 2.96
F1 maximize volume F2 maximize exposed area F3 maximize ground level exposure Convergence Graph
G 3.3
Fitness Value 2.500
G 3.7
Fitness Value 2.699
F1 F2 F3 Comparison Fitness Criteria 2.5
2
1.5
G 3.4
Fitness Value 2.551
G 3.8
Fitness Value 2.770
1
0.5
0
0
0.5
1
1.5
2
2.5
Mean Fitness Value Standard Deviation factor
3
3.5
2.649 0.182 19
6TH GENERATION G 6.1
Fitness Value 2.426
G 6.5
Fitness Value 2.600
G 6.9
Fitness Value 2.901
Strategy 0.50 0.25 0.50 0.60 10
Elitism Mutation Probability Mutation Rate Crossover Rate Population Size Fitness Criteria
G 6.2
Fitness Value 2.499
G 6.6
Fitness Value 2.607
G 6.10
Fitness Value 2.995
F1 maximize volume F2 maximize exposed area F3 maximize ground level exposure Convergence Graph
G 6.3
Fitness Value 2.526
G 6.7
Fitness Value 2.689
F1 F2 F3 Comparison Fitness Criteria 2.5
2
1.5
G 6.4
Fitness Value 2.538
G 6.8
Fitness Value 2.763
1
0.5
0
0
0.5
1
1.5
2
2.5
Mean Fitness Value Standard Deviation factor 20
3
3.5
2.654 0.173
10TH GENERATION G 10.1
Fitness Value 2.261
G 10.5
Fitness Value 2.558
G 10.9
Fitness Value 2.734
Strategy 0.20 0.50 0.50 0.80 10
Elitism Mutation Probability Mutation Rate Crossover Rate Population Size Fitness Criteria
G 10.2
Fitness Value 2.336
G 10.6
Fitness Value 2.641
G 10.10
Fitness Value 2.820
F1 maximize volume F2 maximize exposed area F3 maximize ground level exposure Convergence Graph
G 10.3
Fitness Value 2.412
G 10.7
Fitness Value 2.728
F1 F2 F3 Comparison Fitness Criteria 2.5
2
1.5
G 10.4
Fitness Value 2.573
G 10.8
Fitness Value 2.729
1
0.5
0
0
0.5
1
1.5
2
2.5
Mean Fitness Value Standard Deviation factor
3
3.5
2.579 0.179 21
4.0 | SEQUENCE 04 [MANHATTAN COMMISSIONERS’ PLAN - 1811]
INTRODUCTION
STRATEGY
As an extension of Sequence 03 and the Manhattan Blocks’ evolution, sequence 04 was developed not only to instate the gained knowledge, but also to present an innovative alternative to break the orthogonal grid monotony and to reinvent the New York urban blocks relation.
Break the orthonormal grid by shifting the blocks along the x-axis which creates open spaces with varying dimensions.
STRATEGY
We considered areas with more than 2000m2 as favorites to have direct sun light by trimming buildings with a selected vector.
22
easier pedestrian movement in the new urban tissue. To achieve the desired variation, we run 60 generation with 30 individuals each, focusing on maintaining the maximum exploitation in the begging of the experiment, by reducing the elitism to 0.10 and gradually increasing it to 0.50 thought the generations.
Regarding mutation, our strategy was “The order of the city does not demand the to gradually increase the value to allow impression of endlessness; this impression a greater diversity. The crossover rate can be minimized without interfering with fluctuated between 0.50 and 0.70. functional order. Indeed, by so doing , the really significant attribute of intensity is BODY PLAN AND GENES reinforced” 4 The body plan for sequence 04 was In order to determine a relation between designed with the same concept as the blocks, our strategy was divided in 3 Sequence 03. It is constituted by two main main concepts: parts, the higher and the smaller buildings, which are either facing the avenues or the 1- Randomly shift the grid along the x-axis streets, respectively. to create open spaces The gene pool of Sequence 04 is 2- Evaluate the new open spaces and consisted of six genes with different roles consider areas with more than 2000m2 in the individual genome. By analysing as favourites to have direct sun light by the lack of phenotypical diversity that we trimming the parts of the buildings that acquired by the genes in Sequence 03, prevent direct solar exposure in these we expanded the height genes domain, in areas according to a selected vector. order to reach 120 meters when facing the 3- Create transversal pedestrian paths avenues and 40 meters when facing the through the blocks, which would allow an streets. In addition to that, according to our
Create pedestrian paths inside the blocks
strategy, two new genes were introduced – “blocks shift” and “building trimming”. FITNESS CRITERIA
FITNESS CRITERIA F1 INCREASE DENSITY
For this sequence, the complexity of the fitness criteria was augmented and new conflicting parameters were introduced and evaluated. Increase urban density, increase courtyard area and increase ground level solar exposure (including a 45º degree vector as sun and trimmed buildings). The definition also provides for the creation of a pedestrian footpath in each block, always connecting the streets following a different path. The algorithm evaluates this relation to be assigned as an extra parameter.
F2 INCREASE COURTYARD AREA
F3 INCREASE GROUND LEVEL SOLAR EXPOSURE
In order to have an adequate range to analyse the evolution of the superblocks thought the generations, we evaluate five individuals per generation, precisely the three most fit and the two less fit. As in Sequence 03, it is difficult to visually compare the phenotypes. For this reason, we introduced the radar chats to evaluate not only the fitness criteria average, but also them separately. In our case, the lower the average number, the better.
1st GENERATION In our experiment, we paused the solver at the first generation to extract data from the phenotypes to be able to compare with the future generations. 15th GENERATION By analysing the graphs, the fittest individuals of this generation are in balance between the three criteria. On the other hand, for the less fit, the courtyard area is less than the other individuals. In comparison with generation 1, generation 15 had a rapidly growth related to the average of all fitness criteria. However, the variation decreased. 30th GENERATION In this generation, despite the mean fitness value was increased, the individual evaluation shows a different case related to the balance of the fitness criteria. The Fittest individual has an unbalanced distribution for criteria one and two, density and ground level exposure, respectively. By analysing the convergence graphs, it is possible to understand that, for all fitness criteria, the convergence happened faster than predicted and restricted, again, the diversity.
23
GENE POOL
BODY PLAN
A - Height 60.0m to 120.0m [1] B - Trimming the buildings [1] C - Height 5.0m to 40.0m [2]
[1]
D - Offset 0.0 to 6.0m [2] E - Offset 0.0m to 5.0m [2] F - Blocks Shift -20.0m to 20m [3]
A
B
[2]
D
[3] F E
60 Generations 30 individuals per generation 24
C
45th GENERATION AND 60th GENERATION
COMPARISON FITNESS CRITERIA
Once the gene variety was lost in past generation, we could not reach different results in these last experiments. Evaluating the Fittest and the less fit individuals for these generations, they present similar phenotypes and data for fitness criteria.
7 Generation 1
Generation 15
Generation 30
Generation 45
Generation 60
SERIES1
Series 15
Series 30
Series 45
Series 60
6
5
Between generation 45 and 60, it was possible to notice a slight convergence when analysing the mean fitness value. However, between individuals the visual differences are almost unnoticeable.
4
3
2
COMPARISON To compare the fittest individuals for each generation, we set a variety of parameters – Plot Coverage, FAR, Height, Sun Exposure, Density, Courtyard and Ground Level Exposure. As mentioned above, our generations are lacking visual diversity, which could be clearly seen on the comparison. The numbers are similar from the 30th generation but demonstrate that the individuals evolved significantly when compared to the primitive block and the 1st generation.
1
0
0
0.2
0.4
1st Generation Mean Fitness Value Standard Deviation factor 15th Generation Mean Fitness Value Standard Deviation factor 30th Generation Mean Fitness Value Standard Deviation factor
0.6
0.8
1
1.2
1.4
1.6
1.8
0.950 0.192
45th Generation Mean Fitness Value Standard Deviation factor
0.613 0.120
0.638 0.063
60th Generation Mean Fitness Value Standard Deviation factor
0.605 0.114
0.597 0.113
OBSERVATIONS We concluded from Sequence 03 that the limited gene pool domain was the restriction for our lack in phenotype diversity. By changing these parameters and following a different strategy, we achieved slightly better results but we could not reach the desired variety. In this case, we can conclude that not only the genes, but also the block configuration, the simplicity of the body plan and the relation between the fitness criteria operated as barriers and did not allow as to achieve great phenotypical differences JACOBS, Jane.The Death and Life of Great American Cities,1961.Pg 380 4
25
1ST GENERATION G1.1
G1.29
Fitness Value 0.539
Strategy
Fitness Value 1.299
0.10 0.10 0.50 0.50 30
Elitism Mutation Probability Mutation Rate Crossover Rate Population Size Fitness Criteria
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
1
3
G1.2
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
2
1
3
G1.30
Fitness Value 0.661
F1 increase density F2 increase courtyard F3 increase ground level solar exposure
2
Fitness Value 1.360
Comparison Fitness Criteria 7
6
5
4
3
2
1
0
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
26
3
1
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
2
3
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
1
Mean Fitness Value Standard Deviation factor 2
0.950 0.192
15TH GENERATION G15.1
G15.29
Fitness Value 0.472
Strategy
Fitness Value 0.750
0.10 0.10 0.50 0.50 30
Elitism Mutation Probability Mutation Rate Crossover Rate Population Size Fitness Criteria
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
1
3
G15.2
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
2
1
Convergence Graph
3
G15.30
Fitness Value 0.500
F1 increase density F2 increase courtyard F3 increase ground level solar exposure
F1 F2 F3
2
Fitness Value 0.775
Comparison Fitness Criteria 7
6
5
4
3
2
1
0
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
3
1
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
2
3
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
1
Mean Fitness Value Standard Deviation factor 2
0.638 0.063 27
30TH GENERATION G30.1
G30.29
Fitness Value 0.315
Strategy
Fitness Value 0.764
0.30 0.20 0.50 0.50 30
Elitism Mutation Probability Mutation Rate Crossover Rate Population Size Fitness Criteria
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
1
3
G30.2
1
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
G30.30
Fitness Value 0.431
Convergence Graph
3
2
F1 increase density F2 increase courtyard F3 increase ground level solar exposure
F1 F2 F3
2
Fitness Value 0.773
Comparison Fitness Criteria 7
6
5
4
3
2
1
0
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
28
3
1
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
2
3
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
1
Mean Fitness Value Standard Deviation factor 2
0.597 0.113
45TH GENERATION G45.1
G45.29
Fitness Value 0.424
Strategy
Fitness Value 0.742
0.50 0.30 0.50 0.70 30
Elitism Mutation Probability Mutation Rate Crossover Rate Population Size Fitness Criteria
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
1
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
3
G45.2
2
1
Convergence Graph
3
G45.30
Fitness Value 0.437
F1 increase density F2 increase courtyard F3 increase ground level solar exposure
F1 F2 F3
2
Fitness Value 0.882
Comparison Fitness Criteria 7
6
5
4
3
2
1
0
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
3
1
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
2
3
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
1
Mean Fitness Value Standard Deviation factor 2
0.613 0.120 29
60TH GENERATION G60.1
G60.29
Fitness Value 0.412
Strategy
Fitness Value 0.744
0.20 0.20 0.50 0.50 30
Elitism Mutation Probability Mutation Rate Crossover Rate Population Size Fitness Criteria
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
1
3
G60.2
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
G60.30
Fitness Value 0.431
1
Convergence Graph
3
2
F1 increase density F2 increase courtyard F3 increase ground level solar exposure
F1 F2 F3
2
Fitness Value 0.878
Comparison Fitness Criteria 7
6
5
4
3
2
1
0
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
30
3
1
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
2
3
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
1
Mean Fitness Value Standard Deviation factor 2
0.605 0.114
COMPARISON
Block:
Block Length: Block Width: Street Width: Building Depth:
Plot coverage: FAR: Height [storey]:
primitive
Block:
2-G15.1
Block:
3-G30.1
Block:
4-G45.1
Block:
5-G60.1
790 m 228 m 12 m [maximum] 40 m [minimum] 20 m
Block Length: Block Width: Street Width: Building Depth:
790 m 228 m 12 m [maximum] 40 m [minimum] 20 m
Block Length: Block Width: Street Width: Building Depth:
789 m 228 m 12 m [maximum] 41 m [minimum] 20 m
Block Length: Block Width: Street Width: Building Depth:
788 m 228 m 12 m [maximum] 40 m [minimum] 20 m
Block Length: Block Width: Street Width: Building Depth:
794 m 228 m 12 m [maximum] 44 m [minimum] 20 m
100% 18.30 0 [minimum]
Plot coverage: FAR: Height [storey]:
30 [maximum]
Sun Exposure:
83% Building
Courtyard GL exposure
100% 0% 58%
15.43 0 [minimum]
Plot coverage: FAR: Height [storey]:
30 [maximum]
Sun Exposure:
62% Ground Level
Density
71%
72% Building
Courtyard GL exposure
68% 71% 82%
15.20 0 [minimum]
Plot coverage: FAR: Height [storey]:
30 [maximum]
Sun Exposure:
82% Ground Level
Density
69%
74% Building
Courtyard GL exposure
72% 76% 94%
14.28 0 [minimum]
Plot coverage: FAR: Height [storey]:
30 [maximum]
Sun Exposure:
96% Ground Level
Density
64%
72% Building
Courtyard GL exposure
72% 74% 83%
15.51 0 [minimum] 30 [maximum]
Sun Exposure:
83% Ground Level
Density
61%
71% Building 86% Ground Level
Density Courtyard GL exposure
74% 74% 82%
31
CONCLUSION The use of evolutionary solvers in architectural design is a well-established technique that still has a lot of space for exploration. The way in which they operate is guided by the evolutionary biological dynamics, translated into an abstract digital environment. The solver keeps track on the way that the various forms are breeding and the way the individuals are passing their virtual genes to their offsprings in order to create a fitter generation, according to multiple preset fitness criteria. This design technique can be used in order to explore an increasingly complex space in which it is impossible for the designer to consider in advance all the possible solutions for a certain urban problem. In the urban context, the results that these solvers can provide us may surprise. It would be rather meaningless if the designer could predict all the possible outcomes or the fittest individual of such an evolutionary design process. Although that an evolved form is realised and observed in individual entities, the evolutionary method of thinking implies that the population and not the individual is the matrix for the production and alteration of form. The conducted research throughout the four Sequences followed the evolutionary logic, as expressed in EvoDevo. For the first two Sequences, without the use of evolutionary solvers and for the other two we incorporated the Octopus plugin for Grasshopper. Comparing the results of our experiments in the last sequence, where we analysed a superblock as part of an actual urban environment, we can conclude that the simplicity of our body plan did not allow significant phenotypical diversity, although we keep on having fitter individuals thought the generations. 32
BIBLIOGRAPHY
MENGES, Achim. AHLQUIST, Sean. Computational Design Thinking. Variational Evolution. AD Reader, 2011 DELEUZE, Gilles. GUATTARI, Felix. A Thousand Plateaus. University of Minnesota Press, Minneapolis, 1987 KOOLHAAS, Rem. Delirious New York. Oxford University Press, 1978 JACOBS, Jane.The Death and Life of Great American Cities,1961 WEINSTOCK, Michael. The Architecture of Emergence. John Wiley and Sons, 2010 CARROLL, Sean B. Endless Forms Most Besutiful. Weidenfeld & Nicolson, 2006 Log 25. Reclaim, Resi[lience]stance.,2012 DELANDA, Manuel. Deleuze and the Use of the Genectic Algorithm in Architecture
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