Emergence project by Jens Pedersen

EMERGENCE SEMINAR-GROUP 10

Emergent Technologies & Design 2010-2011

Gabriel Ivorra Morell, Nicolรกs Villegas Giorgi, Nicolรกs Leguina de Enterria, Jens Pedersen

EMERGENCE SEMINAR-GROUP 10

index

GENERAL INTRODUCTION SEQUENCIAL DIAGRAM GENERATIONS’ DIAGRAM SEQUENCE 01_INTRODUCTION INTRODUCTION TO DATA ANALYSIS SEQUENCE 01_GENERATION 01 SEQUENCE 01_GENERATION 02 SEQUENCE 01_GENERATION 03 SEQUENCE 01_GENERATION 04 SEQUENCE 01_GENERATION 05 SEQUENCE 01_GENERATION 06 SEQUENCE 01_EVALUATION SEQUENCE 02_INTRODUCTION SEQUENCE 02_GENERATION 07 SEQUENCE 02_GENERATION 08 SEQUENCE 02_GENERATION 09 SEQUENCE 02_EVALUATION SEQUENCE 03_INTRODUCTION SEQUENCE 03_GENERATION 10 SEQUENCE 03_GENERATION 11 SEQUENCE 03_EVALUATION GENERATION SUMMARY GENERAL EVALUATION AND CONCLUSIONS SCIENTIFIC ANALOGIES AND CONCLUSIONS SCRIPTING APPENDIX Emergent Technologies & Design 2010-2011

03 04 05 06 07 08 11 14 17 20 23 30 31 32 35 38 41 42 43 46 49 50 51 53 54

Gabriel Ivorra Morell, Nicolás Villegas Giorgi, Nicolás Leguina de Enterria, Jens Pedersen

EMERGENCE SEMINAR-GROUP 10

Primitive = basic cell

initial ratio =

Basic cell cluster

Quadrant

general introduction

Quadrant configuration as growth strategy: Father Box

distance centroid - closest face distance centroid - longest face failed attempt

local centroid

global centroid local centroid

The following paper is an account of an experiment for the emergence seminar in the Emtech program(2010-2011) of the Architectural Association. The experiment is an attempt to gain a deep understanding of the underlying processes and forces behind evolution, embryological development, and their application in design by means of genetic algorithms. The basic outline for such is experiment is that by generating various populations and by applying certain constraints and controls we will be able to mimic natural evolution and in turn gain control over it, so as to obtain the desired results.

The starting point for our case was that given a simple platonic figure such as a box and given a simple set of transformations, scale, copy, and move, we could by and iterative and additive process generate enough populations where a fitness criteria could be applied to be able to observe certain “evolution” in a given direction.

L = Longest distance s = Shortest distance

Emergent Technologies & Design 2010-2011

By controlling such fitness criteria and mimicking certain naturally occurring phenomena, such as sexual reproduction, and mutation we could gain a certain control over the global evolutionary process, which in turn would mean that we could harness evolutionary power to be applied in the creation of an emergent design process.

Our first attempts at this generative process where done by hand. We generated a “father” box that contained eight distinct parts, and we then proceeded to generate populations by manually applying the transformations; first to the individual parts; and then at a more global scale. It soon was discovered that due to the fact that we were applying certain transformations to try to obtain a given end result, and that the fitness criteria was complicated in excess, the amount of boxes that were being generated increased at such a rate that the populations soon became unmanageable. From this firsts failed attempts a few things became obvious. Setting the correct fitness criteria is extremely important since it is the main control mechanism that is available to direct the evolutionary process in a given direction. Trying to generate transformations with an idea of what you want in future generations is the easiest way of increasing the complexity of the process without gaining any control over it. Changing the order of the transformations (translocation mutation) can easily generate extreme individuals.

With all this in mind we decided to re-start the whole process with the aid of computational tools that would enable us to eliminate the human factor and exert control by only utilising tools that mirrored the natural occurring control mechanisms in nature, such as environmental pressure, cross breeding, mutations, etcetera. Gabriel Ivorra Morell, Nicolás Villegas Giorgi, Nicolás Leguina de Enterria, Jens Pedersen

EMERGENCE SEMINAR-GROUP 10

SEQUENCIAL DIAGRAM

sequence 01

sequence 02

sequence 03

box body plan generation 01 asexual reproduction

ranking fittest

generation 02 asexual reproduction

ranking

FITTEST SEQUENCE 01

FITTEST SEQUENCE 02

FITTEST GENOME

UNFITTEST SEQUENCE 02

COMPUTATIONAL MUTATIONS

COMPUTATIONAL MUTATIONS + breeding

fittest computational mutations

generation 03 asexual reproduction

ranking

ranking fittest

MUTATIONS generation 10 ranking fittest & UNFITTEST

generation 08

fittest computational mutations

generation 04 asexual reproduction

BREEDING

generation 07

ranking

COMPUTATIONAL MUTATIONS + breeding

fittest

BREEDING

GENOME

fittest GEN. 04 + GEN. 03

generation 09

generation 05

ranking ranking

fittest

SEXUAL REPRODUCTION

MUTATIONS generation 11 ranking fittest & UNFITTEST

FITTEST SEQUENCE 02

generation 06 ranking fittest

Emergent Technologies & Design 2010-2011

UNFITTEST SEQUENCE 02

evaluation & conclusions

Gabriel Ivorra Morell, Nicolรกs Villegas Giorgi, Nicolรกs Leguina de Enterria, Jens Pedersen

EMERGENCE SEMINAR-GROUP 10

GENERATIONS’ DIAGRAM

generation generation generation generation generation 01 02 03 04 05

chimeras

generation generation generation generation generation generation generation generation 06.1 06.2 06 07 08 09 10 11

Emergent Technologies & Design 2010-2011

Gabriel Ivorra Morell, Nicolás Villegas Giorgi, Nicolás Leguina de Enterria, Jens Pedersen

EMERGENCE SEMINAR-GROUP 10

sequence 01_introduction As mentioned before it was decided that the human factor should be limited to setting the environmental pressure (fitness criteria), deciding when and how to cross breed, and when and how to mutate.

father box configuration “Upper” body plan

s.1 s.2 s.3 s.4

i.1 i.2 i.3 i.4

“Lower” body plan

a+b 2

Ratio for sequence 01 =

x c

For this first sequence the aim was to generate enough genetic material to allow for investigation in the following sequences. As in the previous attempt it was decided that all the generations should “evolve” from an original “father” box consisting of 8 boxes, so the first and only set of not random instructions were to copy the simple platonic figure eight times to generate a cubic shape. This generated a box with a given body plan that was coded in the following way: i1, i2, i3, i4, s1, s2, s3, s4 Such an arrangement enabled us to apply independent transformations to the distinct body parts and to code them in an organised fashion; and in further sequences to try to generate a hierarchical and differentiated organisation. It also enabled us to store the set of instructions for each body part and thus recreate any given individual by tracing such instructions back to the “father” box. Once we had the “father” box we set out to generate random instructions to apply to it in order to generate different individuals. To be able to implement such a thing the creation of a VBNET code that based on random numbers generated uncorrelated transformations on each body part was needed. The inputs for such a code were, the body parts, the distance for the move and copy transformations (which would grow as the individuals increased in size going from 0.5 to 2.5) and an horizontal plane at the centroid of the individual. The pseudo code was as follows:

If Random number1> Random number2 then For each box in I1 Copy ((a1*n)X,(b1*n)Y,(c1*n)Z)

centroid

Emergent Technologies & Design 2010-2011

where a1,b1,c1 are random integers between -1 , and 1 n is the input value and X,Y,and Z the coordinates of the plane End for If Random number2> Random number3 then For each box in I1 Move ((a2*n)X,(b2*n)Y,(c2*n)Z) where a2,b2,c2 are random integers between -1, and 1, n is the input value and X,Y,and Z the coordinates of the plane. End for

If Random number3> Random number4 then For each box in I1 Scale ((a3)X,(b3)Y,(b3)Z) where a3,b3,c3 are random integers between 0, and 2, and X,Y,and Z the coordinates of the plane. End for

This was applied to each different body plan This enabled the creation of individuals with randomly generated sets of instructions, where no single set was the same. In effect what we had created was a genome generator that gave distinct non repetitive individuals. The next step was to set a fitness criteria that would provide us with killing strategy so an evolutionary trend in a certain direction could be observed. The most efficient way of setting a fitness was by finding a ratio within the geometry of the individuals that could be easily measured. Such ratio was the average of the length and width of the base of the bounding box, divided by the height, and our fit individuals would be those with the lowest ratio, so in effect what we were doing was selecting the taller and slender individuals. Once the random genome generator was working, and the fitness criteria was set, it was all a question of generating enough populations to see an evolutionary trend. To be able to appreciate such trend the fit gene code needed to be passed on while the unfit gene code was left behind. To attain such a thing the following approach was taken. After the generation of each population the 15 individuals where ranked and the fittest one was used as an input for the genome generator. What this meant in practice was that the set of instructions that generated the fittest individual were passed on since the new set of randomly generated instructions were applied on to these previously selected ones. In relation to natural evolution this was a process of asexual reproduction were new code was added after each generation, and each generation was fitter than the previous one despite the fact that “variation was unrelated to the direction of evolutionary change” (Gould 1977). This process was ran for four generations adding new code and selecting due to the fitness criteria to obtain a genome that could generate very fit individuals. Generation 5 was a collection of the fittest individuals of generations 3 and 4. The four fittest individuals from generation 5 were then selected to be crossbred to obtain 4 chimeras. The 2 fittest were selected to generate 15 individuals each, from which the 15 fittest were selected to constitute population 6, from which the fittest was in turn selected to use its genome as an input for the following sequences.

Gabriel Ivorra Morell, Nicolás Villegas Giorgi, Nicolás Leguina de Enterria, Jens Pedersen

EMERGENCE SEMINAR-GROUP 10

introduction TO DATA ANALYSIS

DEVELOPMENT (RESULT)

Transformations Precedent Development of the parts

The data analysis for this experiment consists of four graphs showing the behaviour of every generation according to the specified criteria. The graph labelled Individual Ratio relates each individual to its ratio and maps the diversity range of the given generation. The Point Distribution graph, explains the situation of each individual relative to the rest of the population. Although one may infer the overall behaviour of the information from these types of graphs, they can only provide relevant information on how strongly related are those specific individuals to each other, according to a specified criterion in a specific situation.

The Statistical Simulation graph represents the behaviour of a population of any size sorted with the same criteria. The graph shows the probability of obtaining an individual with a specific ratio. In order to compare two or more populations, besides using the same criteria, it is necessary have information on how often similar results are likely to be obtain.

In order to record the transformations applied to each individual and to correctly express the taxonomy of each subject, the system has tow quadrants and 8 rows showing the colour assign to each part of the body plan. Then, each of the 3 columns in the middle corresponds to a direction on the Cartesian plane. In this way, it is possible to exactly trace the origin of any particular individual back to the original primitive. Emergent Technologies & Design 2010-2011

2 3

TRANSFORMATION

STEP

FROM

DEVELOPMENT

The Normal Distribution for 15 Individuals shows the incidence of each occurrence in a population that behaves according to the standard deviation and the average of the ratios of 15 individuals.

QUADRANT

BODY PLAN

INDIVIDUAL

GENERATION

Hierachies

PRIMITIVE

TYPE NULL

X Y Z NULL NULL NULL

NULL

NULL NULL NULL

NULL

NULL NULL NULL

NULL

NULL NULL NULL

NULL

NULL NULL NULL

NULL

NULL NULL NULL

NULL

NULL NULL NULL

NULL

NULL NULL NULL

1 COPY 0.5 0.5 0 2 SCALE 2 1 1 NULL NULL NULL NULL NULL

CC/CF

Data

1 1 1 1 1

1.00

1 1 1 3 0

Gabriel Ivorra Morell, Nicolรกs Villegas Giorgi, Nicolรกs Leguina de Enterria, Jens Pedersen

COPY

-0.5

individual 02 I

individual 03 I

individual 04

1 I

Emergent Technologies & Design 2010-2011

X Y Z NULL NULL NULL

NULL

NULL NULL NULL

NULL

NULL NULL NULL

NULL

NULL NULL NULL

NULL

NULL NULL NULL

NULL

NULL NULL NULL

NULL

NULL NULL NULL

NULL

NULL NULL NULL

1 COPY 0.5 0.5 0 2 SCALE 2 1 1 NULL NULL NULL NULL NULL

G01-IN01

PRIMITIVE

TYPE NULL

G01-IN01

COPY

-0.5

SCALE

NULL

1 2 1 2 1 2 1 2 1 2 1 2 1

MOVE SCALE COPY SCALE MOVE COPY MOVE SCALE MOVE SCALE MOVE SCALE MOVE

-0.5 1 -0.5 2 -0.5 0 -0.5 2 -0.5 2 0.5 2 0

0.5 2 0.5 1 0.5 0 0 1 0 2 -0.5 1 0

0 1 0 2 0 0 0.5 2 0.5 2 0.5 1 0.5

1 2 1

MOVE SCALE SCALE

-0.5 2 2

0 2 1

0.5 1 2

1 2 1 2 1

MOVE COPY MOVE COPY COPY

0 -0.5 0 0.5 -0.5

-0.5 0.5 0 0 -0.5

0 -0.5 0 -0.5 -0.5

NULL

COPY

-0.5

0.5

MOVE

-0.5

0.5

COPY

0.5

1 2 1 2

MOVE COPY MOVE COPY

0.5 0.5 0.5 -0.5

0 0.5 -0.5 0.5

0 -0.5 0 -0.5

NULL NULL NULL

1.00

5 2

individual 06

3 0

1.14

6 2

individual 07

1.00

7 2

individual 08

2 0

2 2 1

1.00

2 3 3

G01-IN01

TYPE COPY

X 0

Y 0

Z 0

1 2 1

MOVE COPY COPY

0 -0.5 -0.5

-0.5 -0.5 0.5

-0.5 0 0.5

1 2 1 2 NULL

COPY -0.5 -0.5 0.5 SCALE 2 2 1 MOVE 0.5 0.5 -0.5 COPY -0.5 -0.5 0.5 NULL NULL NULL NULL

1 2 1 2 1 2 1

MOVE COPY MOVE COPY MOVE COPY COPY

0.5 0.5 -0.5 -0.5 -0.5 0.5 0.5

0 0.5 -0.5 -0.5 -0.5 0 0

0 -0.5 -0.5 0.5 0 0 0.5

SCALE

1 2 1 2 1

MOVE SCALE COPY SCALE COPY

0 2 0.5 1 1

0 1 0.5 2 2

-0.5 2 0 1 2

COPY

0.5

-0.5

1 2 1

MOVE COPY COPY

-0.5 0.5 0.5

-0.5 0 0

0 0 0.5

SCALE

1 2 1 2 1

MOVE SCALE COPY SCALE SCALE

0 2 0.5 1 1

0 1 0.5 2 2

-0.5 2 0 1 2

COPY

0.5

-0.5

1 2 1

MOVE SCALE COPY

0 2 0.5

-0.5 2 0

-0.5 1 0

1 2 3 1 2 3 1 2 1 2 1 2 1

MOVE COPY SCALE MOVE COPY SCALE MOVE SCALE MOVE SCALE MOVE COPY COPY

-0.5 -0.5 2 0.5 0.5 1 0.5 1 0 1 0 -0.5 0

0.5 0.5 1 0.5 0 2 0 1 0.5 1 -0.5 0 0.5

0 0 2 0 0.5 2 0 2 0 2 -0.5 0 0

CC/CF

DEVELOPMENT (RESULT)

TRANSFORMATION

STEP

individual 05

FROM

BODY PLAN

INDIVIDUAL

GENERATION

CC/CF

DEVELOPMENT (RESULT)

TRANSFORMATION

STEP

FROM

DEVELOPMENT

QUADRANT

DEVELOPMENT

individual 01

QUADRANT

BODY PLAN

SEQUENCE 01 - GENERATION 01

INDIVIDUAL

GENERATION

EMERGENCE SEMINAR-GROUP 10

0 1 3 3 1

1.20

2 2 3 2 3 2 3 3

1.17

0 3 3 3 2 1 1 2

0.94

3 2 2 1 2 1 1

1.00

1 2 1 2

Gabriel Ivorra Morell, Nicolรกs Villegas Giorgi, Nicolรกs Leguina de Enterria, Jens Pedersen

individual 10

individual 11 I

4 1

individual 12 I

Emergent Technologies & Design 2010-2011

G01-IN01

Z 0

COPY

0.5

1 2 1

MOVE SCALE COPY

0 2 0

-0.5 1 0

-0.5 2 0.5

SCALE

1 2 1

MOVE COPY COPY

-0.5 0 0.5

0.5 0.5 0.5

-0.5 -0.5 0

1 2 1

MOVE SCALE COPY

0 1 0.5

0 1 -0.5

0.5 1 0

1 2 1

MOVE SCALE MOVE

0 2 0.5

-0.5 1 0

0.5 2 0.5

MOVE

-0.5

0.5

SCALE

COPY

-0.5

COPY

0.5

1 2 1

MOVE SCALE COPY

0 1 0.5

0 1 -0.5

0.5 1 0

1 2 1

MOVE SCALE MOVE

0 2 0.5

-0.5 1 0

0.5 2 0.5

MOVE

-0.5

0.5

SCALE

COPY

-0.5

1 2 1 2 1 2 1 2 1

COPY SCALE COPY SCALE COPY SCALE MOVE SCALE COPY

-0.5 2 0.5 2 0.5 2 -0.5 2 0.5

0 1 0.5 1 0 2 -0.5 2 0.5

-0.5 1 0 1 0 2 0.5 2 0.5

COPY

0.5

-0.5

1 2 1

COPY SCALE COPY

0 1 0

0.5 2 0

0.5 2 -0.5

0.94

13 1

individual 14

1 2

0.93

individual 15

2 2

0.88

G01-IN01

TYPE COPY

X 0

Y -0.5

Z -0.5

NULL

COPY

0.5

SCALE

1 2 1

MOVE COPY COPY

-0.5 0 -0.5

0.5 0.5 -0.5

0.5 0 0

NULL

1 2 1

MOVE COPY SACLE

0 -0.5 1

0 -0.5 2

-0.5 -0.5 1

1 2 1

MOVE SCALE SCALE

0 1 1

-0.5 2 2

0 2 1

1 2 1

MOVE COPY SCALE

-0.5 -0.5 1

0.5 0 1

0.5 -0.5 1

NULL NULL NULL

COPY

MOVE

-0.5

MOVE

-0.5

0.5

-0.5

SCALE

1 2 1

MOVE SCALE SCALE

0 1 1

-0.5 2 2

0 2 1

1 2 1

MOVE COPY SCALE

-0.5 -0.5 1

0.5 0 1

0.5 -0.5 1

COPY

MOVE

-0.5

0.5

MOVE

-0.5

0.5

-0.5

CC/CF

DEVELOPMENT (RESULT)

TRANSFORMATION

STEP

individual 13

FROM

DEVELOPMENT

G01-IN01

Y -0.5

BODY PLAN

X 0.5

INDIVIDUAL

TYPE MOVE

GENERATION

CC/CF

DEVELOPMENT (RESULT)

TRANSFORMATION

STEP

FROM

QUADRANT

DEVELOPMENT

individual 09

QUADRANT

BODY PLAN

SEQUENCE 01 - GENERATION 01

INDIVIDUAL

GENERATION

EMERGENCE SEMINAR-GROUP 10

3 3 3 2 2

1.00

1 3 3 2 0 3 2 3

1.17

2 0 2 1 2 1 3 1

0.86

1 2 3

2 0 1 2 2

1.07

2 2 2

Gabriel Ivorra Morell, Nicolรกs Villegas Giorgi, Nicolรกs Leguina de Enterria, Jens Pedersen

EMERGENCE SEMINAR-GROUP 10

statistical simulation

point distribution

#13: ind. 06 ratio: 1.17

#14: ind. 14 ratio: 1.17

#15: ind. 05 ratio: 1.20

fitness evaluation

normal distribution for 15 individuals

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1.00 1.14 1.00 1.00 1.20 1.17 0.94 1.00 0.94 0.93 0.88 1.07 1.00 1.17 0.86

0.11 1.02

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1.00 1.14 1.00 1.00 1.20 1.17 0.94 1.00 0.94 0.93 0.88 1.07 1.00 1.17 0.86

3.62 1.91 3.62 3.62 0.91 1.45 2.78 3.62 2.78 2.60 1.53 3.27 3.62 1.45 1.21

SORT

individual ratio

#12: ind. 02 ratio: 1.14

#07: ind. 03 ratio: 1.00

NORMAL DISTRIBUTION

#11: ind. 12 ratio: 1.07

#06: ind. 01 ratio: 1.00

RATIO COPY

#10: ind. 13 ratio: 1.00

#05: ind. 09 ratio: 0.94

#09: ind. 08 ratio: 1.00

#04: ind. 07 ratio: 0.94

AVERAGE

#03: ind. 10 ratio: 0.93

STANDAR DEVIATION

#02: ind. 11 ratio: 0.88

RATIO

#01: ind. 15 ratio: 0.86

INDIVIDUAL

#08: ind. 04 ratio: 1.00

SEQUENCE 01 - GENERATION 01 parent of generation 02

generation 01 fitness ranking

15 11 10 7 9 1 3 4 8 13 12 2 6 14 5

0.86 0.88 0.93 0.94 0.94 1.00 1.00 1.00 1.00 1.00 1.07 1.14 1.17 1.17 1.20

Here, the 15 individuals have ratios within a range between 0.8 and 1.2. Although the individuals of this population look very different to each other, they all present very similar ratios. In the graphs, 80% of the population can be found tin the upper quarter of the Gaussian bell. In this case, because the bell has a high slope the population is fairly homogenous and has a low dispersion of data.

Emergent Technologies & Design 2010-2011

Gabriel Ivorra Morell, Nicolรกs Villegas Giorgi, Nicolรกs Leguina de Enterria, Jens Pedersen

individual 02 I

individual 03 I

individual 04 I

Emergent Technologies & Design 2010-2011

G01-IN15

MOVE SCALE COPY

-1 2 1

-1 1 0

-1 2 0

MOVE

-1

1 2 1 2 1 2 1 2 1

MOVE SCALE COPY SCALE MOVE SCALE MOVE SCALE COPY

-1 2 -1 2 0 2 0 1 1

0 2 0 2 -1 2 1 2 1

0 1 0 1 1 1 -1 2 -1

SCALE

COPY

-1

1 2 1 2 1

MOVE SCALE MOVE SCALE SCALE

1 1 0 2 1

1 1 0 1 1

0 2 0 1 1

COPY

-1

SCALE

COPY

-1

1 2 1

MOVE SCALE SCALE

-1 2 2

-1 2 1

0 2 1

1 2 1 2 1

MOVE SCALE MOVE SCALE COPY

-1 1 1 1 -1

-1 1 1 2 0

-1 1 0 1 0

1 2 1

MOVE SCALE COPY

0 2 1

0 1 1

-1 2 1

1 2 1 2 1

MOVE COPY MOVE SCALE SCALE

-1 1 1 2 2

1 1 -1 2 2

-1 1 0 2 1

1 2 1 2 1

MOVE SCALE MOVE SCALE COPY

-1 1 1 1 -1

-1 1 1 2 0

-1 1 0 1 0

1 2 1

MOVE SCALE COPY

0 2 1

0 1 1

-1 2 1

1 2

MOVE COPY

-1 1

1 1

-1 1

individual 05

1 6

2 6 0

0.89

1 2

individual 06

1 6 3

1.22

2 2

individual 07

2 6 1

1.12

1 2

individual 08

2 6 2 4 2 3

1.17

G01-IN15

DEVELOPMENT (RESULT)

TYPE

1 2 1

MOVE COPY MOVE

0 1 -1

0 0 -1

-1 0 0

1 2 1

MOVE SCALE COPY

0 1 0

-1 1 1

-1 1 0

1 2 1

MOVE SCALE COPY

0 2 0

0 2 1

COPY

-1

1 2 1 2 1

MOVE COPY MOVE SCALE SCALE

1 0 -1 2 1

0 1 0 1 2

1 -1 0 2 2

1 2 1 2 1

MOVE COPY MOVE COPY SCALE

0 1 1 -1 2

1 -1 1 0 1

0 -1 1 -1 1

COPY

-1

1 2 1 2 1

COPY SCALE MOVE COPY MOVE

0 1 0 -1 0

1 2 0 1 1

0 2 1 1 -1

COPY

-1

SCALE

COPY

SCALE

COPY

-1

1 2 1 2 1

SCALE MOVE MOVE COPY SCALE

2 -1 0 1 2

1 1 -1 -1 1

1 -1 0 1 1

1 2 1

MOVE SCALE MOVE

0 1 1

0 1 -1

0 2 -1

1 2 1

COPY SCALE COPY

-1 2 0

1 2 0

0 1 0

COPY

-1

1 2 1

MOVE SCALE COPY

-1 1 1

1 1 0

-1 2 -1

CC/CF

TRANSFORMATION

STEP

FROM

BODY PLAN

INDIVIDUAL

GENERATION

DEVELOPMENT (RESULT)

TYPE 1 2 1

CC/CF

TRANSFORMATION

STEP

FROM

DEVELOPMENT

QUADRANT

DEVELOPMENT

individual 01

QUADRANT

BODY PLAN

SEQUENCE 01 - GENERATION 02

INDIVIDUAL

GENERATION

EMERGENCE SEMINAR-GROUP 10

3 2 2 6 2

0.84

4 2 3 1 6 2 3 1

0.71

2 2 1 1 4 1 6 1

1.10

4 2 3 1 4 1 9 2

0.57

4 2 2

Gabriel Ivorra Morell, Nicolรกs Villegas Giorgi, Nicolรกs Leguina de Enterria, Jens Pedersen

individual 10 I

individual 11 I

individual 12 I

Emergent Technologies & Design 2010-2011

G01-IN15

-1 1 2

1 1 1

-1 1 2

MOVE

1 2 1

SCALE SCALE MOVE

1 2 0

2 1 0

-1 1 0.5

1 2 1

MOVE SCALE SCALE

-0.5 2 2

0 2 1

0.5 1 2

1 2 1 2 1

MOVE COPY MOVE COPY COPY

0 -0.5 0 0.5 -0.5

-0.5 0.5 0 0 -0.5

0 -0.5 0 -0.5 -0.5

NULL

COPY

-0.5

0.5

MOVE

-0.5

0.5

1 1 1

COPY MOVE COPY

0.5 -0.5 0.5

0 -0.5 0

0 0 0.5

SCALE

1 2 1 2 1

MOVE SCALE COPY SCALE SCALE

0 2 0.5 1 1

0 1 0.5 2 2

-0.5 2 0 1 2

COPY

0.5

-0.5

1 2 1

MOVE SCALE COPY

0 2 0.5

-0.5 2 0

-0.5 1 0

1 2 3 1 2 3 1 2 1 2 1 2 1

MOVE COPY SCALE MOVE COPY SCALE MOVE SCALE MOVE SCALE MOVE COPY COPY

-0.5 -0.5 2 0.5 0.5 1 0.5 1 0 1 0 -0.5 0

0.5 0.5 1 0.5 0 2 0 1 0.5 1 -0.5 0 0.5

0 0 2 0 0.5 2 0 2 0 2 -0.5 0 0

NULL NULL NULL 0

4 3 1

1.03

individual 14

0 4

0.84

14 2

individual 15

1 4

0.68

G01-IN15

DEVELOPMENT (RESULT)

CC/CF

TRANSFORMATION

STEP

FROM

TYPE

individual 13

DEVELOPMENT

QUADRANT

INDIVIDUAL

GENERATION

NULL NULL 1 MOVE 2 SCALE 1 SCALE

BODY PLAN

DEVELOPMENT (RESULT)

TYPE

individual 09

CC/CF

TRANSFORMATION

STEP

DEVELOPMENT

FROM

QUADRANT

BODY PLAN

SEQUENCE 01 - GENERATION 02

INDIVIDUAL

GENERATION

EMERGENCE SEMINAR-GROUP 10

MOVE

0.5

-0.5

COPY

0.5

1 2 1

MOVE SCALE COPY

0 2 0

-0.5 1 0

-0.5 2 0.5

SCALE

1 2 1

MOVE COPY COPY

-0.5 0 0.5

0.5 0.5 0.5

-0.5 -0.5 0

1 2 1

MOVE SCALE COPY

0 1 0.5

0 1 -0.5

0.5 1 0

1 2 1 1 2 1 2 1 2

MOVE SCALE MOVE MOVE SCALE COPY SCALE MOVE COPY

0 2 0.5 -0.5 1 -0.5 2 -0.5 0

-0.5 1 0 0.5 2 0.5 1 0.5 0

0.5 2 0.5 0 1 0 2 0 0

1 2 1 2 1

MOVE SCALE MOVE SCALE COPY

0 2 -1 2 0

1 1 0 1 0

0 1 -1 1 0

MOVE

-1

SCALE

MOVE

0.5

2 4 2 9 2

1.24

0 0 3 2 6 2 0 2

1.24

4 2 3 2 4 2 3 0

0.68

4 3 2

0 4 1 0 3

0.89

4 2 3

Gabriel Ivorra Morell, Nicolรกs Villegas Giorgi, Nicolรกs Leguina de Enterria, Jens Pedersen

EMERGENCE SEMINAR-GROUP 10

SEQUENCE 01 - GENERATION 02 parent of generation 03

generation 02 fitness ranking

#10: ind. 07 ratio: 1.10

#11: ind. 03 ratio: 1.12

#12: ind. 04 ratio: 1.17

#13: ind. 02 ratio: 1.22

statistical simulation

#07: ind. 12 ratio: 0.89

#14: ind. 14 ratio: 1.24

#15: ind. 13 ratio: 1.24

normal distribution for 15 individuals

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

0.89 0.23 0.95 1 0.89 1.70 1.22 2 1.22 0.88 1.12 3 1.12 1.34 1.17 4 1.17 1.07 0.84 5 0.84 1.56 0.71 6 0.71 1.02 1.10 7 1.10 1.43 0.57 8 0.57 0.45 1.03 9 1.03 1.64 0.84 10 0.84 1.55 0.68 11 0.68 0.88 0.89 12 0.89 1.69 1.24 13 1.24 0.75 1.24 14 1.24 0.75 0.68 15 0.68 0.88

SORT

NORMAL DISTRIBUTION

RATIO COPY

fitness evaluation

point distribution

#06: ind. 05 ratio: 0.84

#05: ind. 10 ratio: 0.84

INDIVIDUAL

individual ratio

#04: ind. 06 ratio: 0.71

AVERAGE

#09: ind. 09 ratio: 1.03

#03: ind. 15 ratio: 0.68

STANDAR DEVIATION

#08: ind. 01 ratio: 0.89

#02: ind. 11 ratio: 0.68

RATIO

#01: ind. 08 ratio: 0.57

8 11 15 6 10 5 12 1 9 7 3 4 2 13 14

0.57 0.68 0.68 0.71 0.84 0.84 0.89 0.89 1.03 1.10 1.12 1.17 1.22 1.24 1.24

In this case, the Individual Ratio graph as well as the Point Distribution graph shows a more dispersed series of data. Individuals in this population can occur in the upper half of the Gaussian bell. The Statistical Simulation corroborates this by displaying a wider bell than the one in the previous population, thus this generation is more diverse than generation01.

Emergent Technologies & Design 2010-2011

Gabriel Ivorra Morell, Nicolรกs Villegas Giorgi, Nicolรกs Leguina de Enterria, Jens Pedersen

individual 02 I

individual 03 I

individual 04 I

Emergent Technologies & Design 2010-2011

G02-IN08

COPY

-1.5

COPY

1.5

1 2 1

MOVE SCALE COPY

1.5 2 -1.5

-1.5 2 1.5

1.5 1 1.5

SCALE

COPY

1.5

-1.5

COPY

1.5

-1.5

1.5

COPY

-1.5

1.5

1 2 1

MOVE SCALE SCALE

-1.5 2 2

-1.5 1 2

0 2 2

SCALE

MOVE

-1.5

COPY

-1.5

1.5

-1.5

1 2 1

MOVE SCALE MOVE

-1.5 1 -1.5

0 1 0

-1.5 1 -1.5

MOVE

1.5

1 2 1

MOVE COPY COPY

0 -1.5 0

-1.5 1.5 1.5

-1.5 -1.5 1.5

1 2 1 2 1

MOVE SCALE COPY SCALE COPY

-1.5 2 1.5 2 -1.5

1.5 1 0 2 0

1.5 2 0 2 1.5

MOVE

-1.5

1.5

COPY

1.5

1 2 1 2 1

COPY SCALE MOVE SCALE SCALE

1.5 2 0 2 1

0 1 -1.5 2 1

1.5 1 -1.5 1 1

1 2 1

MOVE COPY SCALE

-1.5 0 2

1.5 1.5 1

0 0 1

SCALE

COPY

individual 05

1 8

2 9 0

0.63

2 4

individual 06

2 18 4

0.89

4 4

individual 07

1 18 2

0.63

4 2

individual 08

2 18 6 8 2 4

0.65

G02-IN08

DEVELOPMENT (RESULT)

TYPE

1 2 1 2 1

MOVE SCALE MOVE SCALE SCALE

0 2 0 1 2

0 2 0 1 1

-1.5 1 1.5 1 2

MOVE

1.5

1 2 1 2 1 2 1 2 1 2 1 2 1

MOVE COPY MOVE COPY COPY SCALE MOVE COPY MOVE COPY MOVE SCALE MOVE

0 1.5 0 1.5 2 2 1.5 1.5 -1.5 0 -1.5 1 0

-1.5 1.5 -1.5 1.5 1 1 -1.5 0 -1.5 0 0 2 1.5

0 1.5 0 1.5 1 1 -1.5 -1.5 0 0 0 2 0

1 2 1

MOVE COPY COPY

0 -1.5 0

1.5 0 0

1.5 0 1.5

MOVE

-1.5

1.5

1 2 1

COPY SCALE COPY

1.5 2 1.5

-1.5 2 0

1.5 2 -1.5

1 2 1

MOVE SCALE SCALE

1.5 2 1

-1.5 1 2

0 2 2

MOVE

-1.5

COPY

1.5

COPY

-1.5

1.5

-1.5

1.5

COPY

NULL

1 2 1

MOVE COPY COPY

1.5 0 0

1.5 0 1.5

-1.5 0 0

COPY

-1.5

1.5

COPY

1.5

COPY

-1.5

COPY

-1.5

1 2 1 2 1

MOVE SCALE MOVE COPY COPY

-1.5 1 1.5 -1.5 -1.5

1.5 2 0 -1.5 0

1.5 1 -1.5 -1.5 1.5

NULL NULL NULL

CC/CF

TRANSFORMATION

STEP

FROM

BODY PLAN

INDIVIDUAL

G02-IN08

SCALE

GENERATION

DEVELOPMENT (RESULT)

TYPE 1

CC/CF

TRANSFORMATION

STEP

FROM

DEVELOPMENT

QUADRANT

DEVELOPMENT

individual 01

QUADRANT

BODY PLAN

SEQUENCE 01 - GENERATION 03

INDIVIDUAL

GENERATION

EMERGENCE SEMINAR-GROUP 10

3 8 1 27 4

0.52

4 2 4 2 8 1 9 6

0.86

6 6 6 3 8 1 36 4

0.64

4 6 4 2 4 1 18 4

0.83

8 0 6

Gabriel Ivorra Morell, Nicolรกs Villegas Giorgi, Nicolรกs Leguina de Enterria, Jens Pedersen

individual 10 I

individual 11 I

individual 12

Emergent Technologies & Design 2010-2011

G02-IN08

MOVE

1.5

-1.5

1 2 1

MOVE COPY SCALE

1.5 -1.5 1

0 0 2

0 -1.5 2

SCALE

NULL

NULL NULL NULL

MOVE

-1.5

SCALE

MOVE

1.5

1 2 1 2 1

MOVE SCALE COPY SCALE COPY

0 2 -1.5 2 0

0 1 1.5 2 1.5

1.5 1 1.5 1 0

MOVE

-1.5

COPY

1.5

-1.5

1.5

NULL

SCALE

NULL NULL NULL 1

COPY

1.5

COPY

-1.5

SCALE

COPY

-1.5

1.5

-1.5

SCALE

1 2 1 2 1 2 1 2 1

MOVE SCALE MOVE SCALE MOVE SCALE MOVE COPY MOVE

1.5 1 0 1 0 2 -1.5 1.5 0

1.5 1 1.5 2 0 1 0 -1.5 1.5

-1.5 1 1.5 2 0 2 -1.5 1.5 -1.5

1 2 1

MOVE SCALE MOVE

1.5 1 0

1.5 1 1.5

0 1 -1.5

MOVE

-1.5

1.5

1 2 1

MOVE SCALE MOVE

-1.5 1 0

0 2 -1.5

1.5 2 1.5

MOVE

1.5

-1.5

COPY

individual 13

2 8

2 18 4

0.74

13 2

individual 14

1 12

0.94

individual 15

2 12

0.94

15 2

G02-IN08

DEVELOPMENT (RESULT)

TYPE

COPY

1.5

SCALE

1 2 1 2 1 2 1 2 1

MOVE COPY MOVE SCALE MOVE COPY MOVE COPY SCALE

1.5 1.5 0 2 0 1.5 1.5 -1.5 1

0 1.5 0 2 -1.5 1.5 0 -1.5 2

0 -1.5 0 1 1.5 1.5 0 1.5 2

1 2 1

MOVE COPY MOVE

1.5 -1.5 -1.5

0 -1.5 -1.5

-1.5 1.5 -1.5

1 2 1 2 1

MOVE SCALE MOVE SCALE MOVE

0 2 1.5 1 1.5

1.5 1 -1.5 2 -1.5

1.5 2 1.5 2 1.5

NULL

NULL NULL NULL

COPY

-1.5

1.5

MOVE

1.5

COPY

-1.5

1.5

MOVE

1.5

-1.5

COPY

1.5

-1.5

COPY

1 2 1 2 1

COPY SACLE COPY SCALE MOVE

-1.5 2 0 2 0

0 1 1.5 1 1.5

0 2 0 1 0

1 2 1

COPY SCALE COPY

1.5 1 -1.5

0 1 1.5

-1.5 1 0

CC/CF

TRANSFORMATION

STEP

FROM

BODY PLAN

INDIVIDUAL

GENERATION

DEVELOPMENT (RESULT)

TYPE 1

CC/CF

TRANSFORMATION

STEP

FROM

DEVELOPMENT

QUADRANT

DEVELOPMENT

individual 09

QUADRANT

BODY PLAN

SEQUENCE 01 - GENERATION 03

INDIVIDUAL

GENERATION

EMERGENCE SEMINAR-GROUP 10

1 8 1 9 4

0.79

4 2 4 2 4 4 16 2

0.51

12 2 6 1 8 2 27 6

0.79

4 6 4

2 4 2 9 4

0.67

8 4 6

Gabriel Ivorra Morell, Nicolรกs Villegas Giorgi, Nicolรกs Leguina de Enterria, Jens Pedersen

EMERGENCE SEMINAR-GROUP 10

SEQUENCE 01 - GENERATION 03 parent of generation 04

generation 03 fitness ranking

#05: ind. 07 ratio: 0.52

#10: ind. 13 ratio: 0.94

#11: ind. 08 ratio: 0.94

#12: ind. 06 ratio: 0.67

#13: ind. 02 ratio: 0.79

#14: ind. 11 ratio: 0.51

statistical simulation

#15: ind. 10 ratio: 0.68

normal distribution for 15 individuals

SORT

RATIO COPY

fitness evaluation

point distribution

#07: ind. 12 ratio: 0.64

#06: ind. 04 ratio: 0.86

INDIVIDUAL

individual ratio

#04: ind. 01 ratio: 0.65

AVERAGE

#09: ind. 15 ratio: 0.74

#03: ind. 03 ratio: 0.63

STANDAR DEVIATION

#08: ind. 09 ratio: 0.83

#02: ind. 05 ratio: 0.89

RATIO

#01: ind. 14 ratio: 0.63

0.51 1 0.63 0.14 0.73 1 0.63 14 0.52 2 0.89 2 0.89 5 0.63 3 0.63 3 0.63 3 0.63 4 0.65 4 0.65 1 0.64 5 0.52 5 0.52 7 0.65 6 0.86 6 0.86 4 0.67 7 0.64 7 0.64 12 0.68 8 0.83 8 0.83 15 0.74 9 0.74 9 0.74 9 0.79 10 0.94 10 0.94 13 0.83 11 0.94 11 0.94 8 0.86 12 0.67 12 0.67 6 0.89 13 0.79 13 0.79 2 0.94 14 0.51 14 0.51 10 0.94 15 0.79 15 0.68 11 The behaviour of this population continues to present the same characteristics as the previous one; however, the Gaussian bell is slightly steeper and a little bit to the right. This means that compared to Generation 02, Generation 03 has an overall “less fit” and more homogeneous population than its predecessor. It is interesting to see that both populations have similar point distributions but different peaks and ranges.

Emergent Technologies & Design 2010-2011

Gabriel Ivorra Morell, Nicolás Villegas Giorgi, Nicolás Leguina de Enterria, Jens Pedersen

individual 02 I

individual 03 I

individual 04 I

Emergent Technologies & Design 2010-2011

G03-IN14

COPY

-2

COPY

-2

COPY

-2

1 2 1

MOVE COPY MOVE

2 -2 2

0 2 -2

-2 -2 -2

NULL

COPY

-2

COPY

-2

SCALE

1 2 1

MOVE SCALE SCALE

2 1 1

0 1 2

-2 1 1

1 2 1 2 1

COPY SCALE MOVE COPY SCALE

0 1 0 0 2

2 2 -2 0 2

2 1 -2 -2 1

COPY

NULL NULL NULL

36 24

G03-IN14

MOVE COPY MOVE

0 2 2

2 2 0

0 -2 0

1 1

MOVE

-2

MOVE

-2

MOVE

-2

1 2 1 2 1

COPY SCALE COPY SCALE COPY

-2 2 2 2 2

0 2 2 1 0

2 1 0 1 -2

1 2 1

COPY SCALE MOVE

2 1 0

0 2 2

0 1 0

SCALE

1 2 1

COPY SCALE COPY

2 1 2

-2 2 -2

0 1 0

0.95

0 12

individual 06

8 18 24

0.55

8 12

individual 07 I

0.62

2 6

individual 08

8 54 8 12 6 12

0.87

G03-IN14

DEVELOPMENT (RESULT)

TYPE

MOVE

-2

MOVE

COPY

1 2 1

MOVE SCALE COPY

-2 2 0

0 2 -2

2 1 0

1 2 1

MOVE SCALE COPY

2 1 0

2 2 2

-2 1 2

MOVE

-2

SCALE

COPY

-2

1 2 1 2 1

COPY SCALE COPY SCALE COPY

2 1 -2 2 -2

2 1 -2 2 2

2 2 -2 2 -2

NULL

1 2 1 2 1

MOVE SCALE MOVE COPY MOVE

NULL NULL NULL 2 2 -2 2 0

-2 1 -2 0 -2

2 2 2 2 2

MOVE

-2

MOVE

-2

1 2 1 2 1 2 1 2 1

MOVE COPY COPY SCALE MOVE SCALE MOVE SCALE COPY

0 -2 0 1 2 1 0 1 -2

2 0 0 2 0 1 0 1 2

2 0 -2 2 -2 1 -2 2 0

COPY

-2

1 2 1

MOVE SCALE MOVE

-2 2 2

-2 1 -2

2 2 -2

COPY

-2

SCALE

MOVE

-2

COPY

-2

CC/CF

TRANSFORMATION

STEP

FROM

BODY PLAN I

1 COPY 0 0 0 6 2 SCALE 2 1 2 1 MOVE 0 -2 0 12 2 COPY 0 0 -2 NULL NULL NULL NULL NULL 0 1 2 1

INDIVIDUAL

individual 05

DEVELOPMENT

-2

QUADRANT

MOVE

GENERATION

DEVELOPMENT (RESULT)

TYPE 1

CC/CF

TRANSFORMATION

STEP

FROM

DEVELOPMENT

individual 01

QUADRANT

BODY PLAN

SEQUENCE 01 - GENERATION 04

INDIVIDUAL

GENERATION

EMERGENCE SEMINAR-GROUP 10

2 4 8 36 16

0.90

24 4 6 2 8 12 54 26

0.71

16 0 18 2 4 4 54 24

0.52

24 4 12 4 8 4 36 8

0.46

12 4 12

Gabriel Ivorra Morell, Nicolรกs Villegas Giorgi, Nicolรกs Leguina de Enterria, Jens Pedersen

individual 10 I

individual 11 I

individual 12 I

Emergent Technologies & Design 2010-2011

NULL

1 2 1

MOVE SCALE SCALE

-2 2 1

-2 1 1

-2 2 1

COPY

1 2 1 2 1 2 1 2 1

MOVE COPY MOVE COPY MOVE COPY MOVE COPY MOVE

2 0 2 2 0 0 2 2 2

0 -2 0 2 2 -2 -2 2 -2

2 -2 2 0 0 0 2 2 2

G03-IN14

MOVE

NULL NULL NULL

-2

COPY

-2

SCALE

MOVE

-2

1 2 1

MOVE SCALE COPY

0 1 0

-2 1 2

2 2 2

1 2 1

MOVE COPY SCALE

-2 -2 2

2 -2 1

-2 2 1

SCALE

1 2 1 2 1

MOVE SCALE MOVE SCALE MOVE

-2 1 0 2 0

2 2 0 2 2

-2 2 2 2 0

COPY

-2

1 2 1

COPY SCALE COPY

-2 2 0

0 2 -2

-2 2 -2

1 2 1

MOVE COPY SCALE

0 -2 1

2 -2 1

2 2 1

SCALE

COPY

-2

COPY

0.62

6 18

individual 14

4 4

36 8

0.53 S

12 4 12

individual 15

18 16

0.83 S

24 2

G03-IN14

DEVELOPMENT (RESULT)

TYPE

COPY

-2

COPY

NULL

1 2 1

MOVE SCALE COPY

-2 1 0

2 2 0

0 1 -2

1 2 1

MOVE COPY COPY

2 -2 -2

2 -2 0

0 0 2

NULL NULL NULL

CC/CF

TRANSFORMATION

STEP

individual 13

FROM

BODY PLAN

INDIVIDUAL

G03-IN14

COPY

GENERATION

DEVELOPMENT (RESULT)

TYPE 1

CC/CF

TRANSFORMATION

STEP

FROM

DEVELOPMENT

QUADRANT

DEVELOPMENT

individual 09

QUADRANT

BODY PLAN

SEQUENCE 01 - GENERATION 04

INDIVIDUAL

GENERATION

EMERGENCE SEMINAR-GROUP 10

4 8 0 36 16

0.74

36 4

1 MOVE 0 2 0 18 2 COPY -2 -2 2 1 MOVE 2 2 0 4 2 SCALE 1 2 1 NULL NULL NULL NULL NULL 0

G03-IN14

SCALE

MOVE

-2

1 2 1

COPY SCALE MOVE

0 2 2

2 2 0

-2 1 0

COPY

-2

1 2 1

MOVE COPY MOVE

-2 2 -2

0 0 0

2 0 -2

SCALE

MOVE

-2

SCALE

COPY

-2

MOVE

COPY

-2

COPY

4 18 24

0.97

12 4 18 4 4 4 18 16

0.79

12 4 12

12 6 8 12 18 8

0.56

12 4 12

Gabriel Ivorra Morell, Nicolรกs Villegas Giorgi, Nicolรกs Leguina de Enterria, Jens Pedersen

EMERGENCE SEMINAR-GROUP 10

SEQUENCE 01 - GENERATION 04

generation 04 fitness ranking

#10: ind. 07 ratio: 1.10

#11: ind. 03 ratio: 1.12 point distribution

#12: ind. 04 ratio: 1.17

#13: ind. 02 #14: ind. 14 ratio: 1.22 ratio: 1.24 fitness evaluation

0.17

0.71

NORMAL DISTRIBUTION

0.95 0.55 0.62 0.87 0.90 0.71 0.52 0.46 0.62 0.53 0.83 0.56 0.74 0.97 0.79

AVERAGE

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

#15: ind. 13 ratio: 1.24

RATIO COPY

normal distribution for 15 individuals

#07: ind. 12 ratio: 0.89

#06: ind. 05 ratio: 0.84

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

0.95 0.55 0.62 0.87 0.90 0.71 0.52 0.46 0.62 0.53 0.83 0.56 0.74 0.97 0.79

0.87 1.48 2.08 1.46 1.24 2.36 1.26 0.83 2.08 1.38 1.82 1.62 2.33 0.73 2.07

SORT

#05: ind. 10 ratio: 0.84

statistical simulation

#04: ind. 06 ratio: 0.71

STANDAR DEVIATION

#09: ind. 09 ratio: 1.03

#03: ind. 15 ratio: 0.68

RATIO

#08: ind. 01 ratio: 0.89 individual ratio

#02: ind. 11 ratio: 0.68

INDIVIDUAL

#01: ind. 08 ratio: 0.57

8 7 10 2 12 3 9 6 13 15 11 4 5 1 14

0.46 0.52 0.53 0.55 0.56 0.62 0.62 0.71 0.74 0.79 0.83 0.87 0.90 0.95 0.97

In this generation the Gaussian bell is remarkable similar to the one shown in for the previous generation, however, the point distribution is surprisingly different. This means that even though individuals from population 3 are very different from individuals from population 4 they all share similar overall behaviours. The population is “as fit” as population 3 however, it displays more diversity.

Emergent Technologies & Design 2010-2011

Gabriel Ivorra Morell, Nicolás Villegas Giorgi, Nicolás Leguina de Enterria, Jens Pedersen

4 36

individual 02

G03-IN14

individual 03

individual 04

Emergent Technologies & Design 2010-2011

COPY

-1.5

SCALE

2.5

1.5

COPY

1.5

SCALE

1.5

COPY

-1.5

SCALE MOVE MOVE COPY SCALE

1.5 1.5 -1.5 -1.5 1.5

-1.5

1.5 -1.5 0 1.5 1.5

0 1.5 1.5

0 1.5 -1.5

0 2.5 -1.5

1 2 1

COPY SCALE COPY

-1.5 2.5 0

1.5 2.5 0

0 1.5 0

COPY MOVE SCALE COPY

NULL NULL 1 MOVE 2 SCALE 1 SCALE

G03-IN14

1.5

MOVE SCALE MOVE

1 2 1

G02-IN8

2.5 -1.5 0 1.5 2.5

1 2 1

G02-IN8

1 2 1 2 1

1.5 -1.5 1.5 1.5

-1.5 1.5 1.5 0

1.5 -1.5 2.5 -1.5

-1.5 1.5 2.5

1.5 1.5 1.5

-1.5 1.5 2.5

MOVE

1.5

1 2 1

SCALE SCALE MOVE

1.5 2.5 0

2.5 1.5 0

-1.5 1.5 0.5

1 2 1

MOVE SCALE SCALE

-0.5 2.5 2.5

0 2.5 1.5

0.5 1.5 2.5

SCALE

1.5

2.5

COPY

-1.5

1 2 1 2 1

MOVE SCALE MOVE SCALE SCALE

1.5 1.5 0 2.5 1.5

1.5 1.5 0 1.5 1.5

0 2.5 0 1.5 1.5

COPY

1 1

1.5

-1.5

1.5

SCALE

1.5

2.5

1.5

COPY

-1.5

4 36

0.46

12 4

individual 06

2.5 I

4 4 18

0.51 S

12 2.5

individual 07 I

8 1.5 27 4

2.5

individual 08

2.5

54 24

0.52 S

G03-IN14

DEVELOPMENT (RESULT)

TYPE

1 2 1

MOVE SCALE SCALE

-1.5 2.5 2.5

-1.5 2.5 1.5

0 2.5 1.5

1 2 1 2 1

MOVE SCALE MOVE SCALE COPY

-1.5 1.5 1.5 1.5 -1.5

-1.5 1.5 1.5 2.5 0

-1.5 1.5 0 1.5 0

1 2 1

MOVE SCALE COPY

0 2.5 1.5

0 1.5 1.5

-1.5 2.5 1.5

1 2 1 2 1

MOVE COPY MOVE SCALE SCALE

-1.5 1.5 1.5 2.5 2.5

1.5 1.5 -1.5 2.5 2.5

-1.5 1.5 0 2.5 1.5

1 2 1 2 1

MOVE SCALE MOVE SCALE COPY

-1.5 1.5 1.5 1.5 -1.5

-1.5 1.5 1.5 2.5 0

-1.5 1.5 0 1.5 0

1 2 1

MOVE SCALE COPY

0 2.5 1.5

0 1.5 1.5

-1.5 2.5 1.5

1 2 1 2 1

MOVE COPY MOVE COPY MOVE

-1.5 1.5 0 1.5 -1.5

1.5 1.5 0 0 -1.5

-1.5 1.5 -1.5 0 0

1 2 1

MOVE SCALE COPY

0 1.5 0

-1.5 1.5 1.5

-1.5 1.5 0

1 2 1

MOVE SCALE COPY

0 2.5 0

0 2.5 1.5

COPY

-1.5

1.5

1 2 1 2 1

MOVE COPY MOVE SCALE SCALE

1.5 0 -1.5 2.5 1.5

0 1.5 0 1.5 2.5

1.5 -1.5 0 2.5 2.5

1 2 1 2 1

MOVE COPY MOVE COPY SCALE

0 1.5 1.5 -1.5 2.5

1.5 -1.5 1.5 0 1.5

0 -1.5 1.5 -1.5 1.5

COPY

-1.5

1 2 1 2

COPY SCALE MOVE COPY

0 1.5 0 -1.5

1.5 2.5 0 1.5

0 2.5 1.5 1.5

CC/CF

TRANSFORMATION

STEP

G03-IN14

0.52

FROM

BODY PLAN

INDIVIDUAL

GENERATION

individual 05

DEVELOPMENT

MOVE

QUADRANT

DEVELOPMENT (RESULT)

TYPE 1

CC/CF

TRANSFORMATION

STEP

FROM

DEVELOPMENT

individual 01

QUADRANT

BODY PLAN

SEQUENCE 01 - GENERATION 05

INDIVIDUAL

GENERATION

EMERGENCE SEMINAR-GROUP 10

6 4 4 36 8

0.53

12 4 12 4 4 8 18 24

0.55

0 8 12 6 8 12 18 0.56 8 12 4 12 6 12 0 54 8

0.62

12 2.5 6

Gabriel Ivorra Morell, Nicolรกs Villegas Giorgi, Nicolรกs Leguina de Enterria, Jens Pedersen

3 4

individual 10 I

individual 11 I

individual 12 I

Emergent Technologies & Design 2010-2011

-1.5

SCALE

2.5

1.5

COPY

1.5

SCALE

1.5

COPY

-1.5

1 2 1 2 1

SCALE MOVE MOVE COPY SCALE

2.5 -1.5 0 1.5 2.5

1.5 1.5 -1.5 -1.5 1.5

1.5 -1.5 0 1.5 1.5

1 2 1

G02-IN8

COPY

G02-IN8

MOVE SCALE MOVE

0 1.5 1.5

0 1.5 -1.5

0 2.5 -1.5

1 2 1

COPY SCALE COPY

-1.5 2.5 0

1.5 2.5 0

0 1.5 0

COPY

1.5

-1.5

1.5

1 2 1

MOVE SCALE COPY

-1.5 1.5 1.5

1.5 1.5 0

-1.5 2.5 -1.5

NULL

1 2 1

MOVE SCALE SCALE

-1.5 1.5 2.5

1.5 1.5 1.5

-1.5 1.5 2.5

MOVE

1.5

1 2 1

SCALE SCALE MOVE

1.5 2.5 0

2.5 1.5 0

-1.5 1.5 0.5

1 2 1

MOVE SCALE SCALE

-0.5 2.5 2.5

0 2.5 1.5

0.5 1.5 2.5

1 2 1 2 1

MOVE COPY MOVE COPY COPY

0 -0.5 0 0.5 -0.5

-0.5 0.5 0 0 -0.5

0 -0.5 0 -0.5 -0.5

NULL NULL NULL

NULL

COPY

NULL NULL NULL 0

COPY

-0.5

0.5

MOVE

-0.5

0.5

1 2

COPY MOVE

0.5 -0.5

0 -0.5

0 0

0.62

6 18

individual 14

8 1.5 18 2

0.63 S

8 4 2

individual 15

1.5

8 2 9 0

0.63

G02-IN8

G03-IN14

DEVELOPMENT (RESULT)

TYPE

COPY

0.5

SCALE

2.5

SCALE

2.5

1.5

1 2 1 2 1

MOVE SCALE COPY SCALE SCALE

0 2.5 0.5 1.5 1.5

0 1.5 0.5 2.5 2.5

-0.5 2.5 0 1.5 2.5

COPY

0.5

-0.5

1 2 1 2 1

MOVE SCALE MOVE SCALE COPY

0 2.5 -1.5 2.5 1.5

-0.5 2.5 -1.5 1.5 0

-0.5 1.5 -1.5 2.5 0

MOVE

-1.5

1 2 1 2 1 2 1 2 1

MOVE SCALE COPY SCALE MOVE SCALE MOVE SCALE COPY

-1.5 2.5 -1.5 2.5 0 2.5 0 1.5 1.5

0 2.5 0 2.5 -1.5 2.5 1.5 2.5 1.5

0 1.5 0 1.5 1.5 1.5 -1.5 2.5 -1.5

SCALE

1.5

2.5

COPY

-1.5

1 2 1 2 1

MOVE SCALE MOVE SCALE SCALE

1.5 1.5 0 2.5 1.5

1.5 1.5 0 1.5 1.5

0 2.5 0 1.5 1.5

COPY

1.5

-1.5

1.5

SCALE

1.5

2.5

1.5

COPY

-1.5

CC/CF

TRANSFORMATION

individual 13

STEP

G03-IN14

-1.5

FROM

1.5

BODY PLAN

INDIVIDUAL

MOVE

GENERATION

DEVELOPMENT (RESULT)

TYPE 1

CC/CF

TRANSFORMATION

STEP

FROM

DEVELOPMENT

QUADRANT

DEVELOPMENT

individual 09

QUADRANT

BODY PLAN

SEQUENCE 01 - GENERATION 05

INDIVIDUAL

GENERATION

EMERGENCE SEMINAR-GROUP 10

1.5 12 2 18 6

0.65

8 2 4 2 4 2 9 4

0.67

8 4 6 2 8 12 54 16

0.71

0 4 18

4 3 8 1.5 36 4

0.64

4 6 4

Gabriel Ivorra Morell, Nicolรกs Villegas Giorgi, Nicolรกs Leguina de Enterria, Jens Pedersen

EMERGENCE SEMINAR-GROUP 10

SEQUENCE 01 - GENERATION 05

#05: ind. 05 ratio: 0.53

#10: ind. 10 ratio: 0.63

#11: ind. 11 ratio: 0.63

#12: ind. 12 ratio: 0.64

#13: ind. 13 ratio: 0.65

#15: ind. 15 ratio: 0.71

#14: ind. 14 ratio: 0.67

normal distribution for 15 individuals

0.46 0.51 0.52 0.52 0.53 0.55 0.56 0.62 0.62 0.63 0.63 0.64 0.65 0.67 0.71

0.07

0.59

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

0.46 0.51 0.52 0.52 0.53 0.55 0.56 0.62 0.62 0.63 0.63 0.64 0.65 0.67 0.71

1.17 3.27 3.40 3.52 4.20 4.68 5.29 5.03 4.99 4.82 4.59 4.56 3.91 2.88 1.40

SORT

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

NORMAL DISTRIBUTION

fitness evaluation

RATIO COPY

point distribution

#07: ind. 07 ratio: 0.56

#06: ind. 06 ratio: 0.55

statistical simulation

#04: ind. 04 ratio: 0.52

AVERAGE

individual ratio

#03: ind. 03 ratio: 0.52

STANDAR DEVIATION

#09: ind. 09 ratio: 0.62

#02: ind. 02 ratio: 0.51

RATIO

#08: ind. 08 ratio: 0.62

#01: ind. 01 ratio: 0.46

INDIVIDUAL

4 chimera generators

generation 05 fitness ranking

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

0.46 0.51 0.52 0.52 0.53 0.55 0.56 0.62 0.62 0.63 0.63 0.64 0.65 0.67 0.71

Emergent Technologies & Design 2010-2011

Gabriel Ivorra Morell, Nicolás Villegas Giorgi, Nicolás Leguina de Enterria, Jens Pedersen

EMERGENCE SEMINAR-GROUP 10 CHIMERAS* GENERATION

SEQUENCE 01 - GENERATION 06

CHIMERAS

GENOME FOR CROSSBREEDING 22 11 11 11

11 IN14-G02

I 13 S

I 14 S

I 15 S

1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4

22 8.00 8.00 8.00 8.00 IN08-G03 8.00 8.00 8.00 8.00 4.00 4.00 4.00 NULL IN02-G03 NULL 4.00 NULL NULL 22.00 11.00 11.00 11.00 IN14-G03 11.00 22.00 11.00 22.00

VOID VOID VOID VOID VOID VOID VOID VOID VOID VOID VOID VOID VOID VOID VOID VOID VOID VOID VOID VOID VOID VOID VOID VOID VOID VOID VOID VOID VOID VOID VOID VOID VOID VOID VOID VOID

NULL NULL NULL NULL NULL NULL NULL NULL NULL NULL NULL NULL NULL NULL NULL NULL NULL NULL NULL NULL NULL NULL NULL NULL NULL NULL NULL NULL NULL NULL NULL NULL NULL NULL NULL NULL

22 11 11 11

VOID NULL 22 individual 07_GENERATION 04 NULL 3 11 VOID NULL 11 4 VOID NULL VOID NULL 1 VOID NULL IN14-G02 0.027478 18.478328 0.001 12 VOID NULL 2 22 22 VOID NULL S VOID NULL 3 VOID NULL VOID NULL 4 22 22 VOID NULL 1 VOID NULL 2 4.00 VOID NULL 4.00 I 3 VOID NULL 4 NULL VOID NULL NULL IN02-G03 0.21 16.97 0.01 14 1 NULL VOID NULL NULL 2 VOID NULL S 3 NULL VOID NULL NULL 4 VOID NULL 1 2

8.00

11 0.027478

18.478328

0.001

0.51

32.65

0.02

22 11 22 8.00 8.00 8.00 8.00 8.00 8.00 8.00 8.00 4.00 4.00 4.00 NULL NULL 4.00 NULL NULL 22.00 11.00 11.00 11.00 11.00 22.00 11.00 22.00

individual 03 2 05_GENERATIONVOID

0.21

0.03

16.97

36.09

VOID VOID VOID 4 VOID IN08-G03 13 1 VOID 2 8.00 VOID S 3 VOID 4 8.00 VOID 1 VOID 2 11.00 VOID I 3 VOID 4 11.00 VOID IN14-G03 15 1 11.00 VOID 2 VOID S 3 11.00 VOID 4 VOID I chimera 01* 3 8.00

0.01

0.00

parent for generation 6.1

NULL NULL NULL NULL NULL NULL NULL NULL NULL NULL NULL NULL NULL NULL NULL NULL

individual 08_GENERATION 04

individual 14_GENERATION 03

CHIMERA-A

1 2 3 4

8.00

chimera 03*

chimera 02* 8.00 0.51

32.65

0.02

0.03

36.09

0.00

chimera 04*

8.00 8.00 11.00 11.00 11.00 11.00

parent for generation 6.2

CHIMERA-B

*In molecular biology, a chimera is an artificial protein created by joining two different protein fragments

Emergent Technologies & Design 2010-2011

Gabriel Ivorra Morell, Nicolรกs Villegas Giorgi, Nicolรกs Leguina de Enterria, Jens Pedersen

EMERGENCE SEMINAR-GROUP 10

SEQUENCE 01 - GENERATION 06_part 01

individual 04

individual 01 1 I

2 3 4

individual 02

4 36

3 4

individual 03

1 I

CHIMERA 1

4 12 Emergent Technologies & Design 2010-2011

CHIMERA 1

DEVELOPMENT (RESULT)

TYPE

SCALE

COPY

-1

1 2 1 2 1

MOVE SCALE MOVE SCALE SCALE

1 1 0 2 1

1 1 0 1 1

0 2 0 1 1

COPY

-1

SCALE

COPY

-1

1 2 1

MOVE SCALE SCALE

-1 2 2

-1 2 1

0 2 1

1 2 1 2 1

MOVE SCALE MOVE SCALE COPY

-1 1 1 1 -1

-1 1 1 2 0

-1 1 0 1 0

1 2 1

MOVE SCALE COPY

0 2 1

0 1 1

-1 2 1

1 2 1

MOVE COPY COPY

-1 1 1

1 1 -1

-1 1 1

SCALE

COPY

-1

1 2 1

MOVE SCALE SCALE

-1 2 2

-1 2 1

0 2 1

1 2 1

MOVE SCALE COPY

0 2 1

0 1 1

-1 2 1

COPY

-1

individual 05

12 54 48

0.30

individual 06

2 8

8 54 48

0.90

individual 07

8 5

48 4 0 8

0.55

3 4

4 12

CHIMERA 1

DEVELOPMENT (RESULT)

TYPE

1 2 1

MOVE SCALE SCALE

-1 2 2

-1 2 1

0 2 1

1 2 1 2 1

MOVE SCALE MOVE SCALE COPY

-1 1 1 1 -1

-1 1 1 2 0

-1 1 0 1 0

1 2 1

MOVE SCALE COPY

0 2 1

0 1 1

-1 2 1

1 2 1

MOVE COPY MOVE

-1 1 0

1 -1 1 1 1.5 -1.5

COPY

-1.5

SCALE

2.5

1.5

COPY

1.5

SCALE

1.5

COPY

-1.5

1 2 1 2 1

SCALE MOVE MOVE COPY SCALE

2.5 -1.5 0 1.5 2.5

1.5 1.5 -1.5 -1.5 1.5

1.5 -1.5 0 1.5 1.5

1 2 1

MOVE SCALE MOVE

0 1.5 1.5

0 1.5 -1.5

0 2.5 -1.5

1 2 1

COPY SCALE COPY

-1.5 2.5 0

1.5 2.5 0

0 1.5 0

COPY

1.5

-1.5

1.5

1 2 1

MOVE SCALE COPY

-1.5 1.5 1.5

1.5 1.5 0

-1.5 2.5 -1.5

1 2 1

MOVE COPY MOVE

0 0 2.5

-2.5 0 -2.5

0 0 -2.5

1 2 1

MOVE COPY MOVE

-2.5 2.5 2.5

0 0 -2.5

-2.5 -2.5 2.5

MOVE

-2.5

SCALE

MOVE

-2.5

2.5

CC/CF

TRANSFORMATION

STEP

FROM

DEVELOPMENT

CC/CF

TRANSFORMATION

STEP

FROM

DEVELOPMENT

QUADRANT

BODY PLAN

INDIVIDUAL

GENERATION

QUADRANT

BODY PLAN

INDIVIDUAL

GENERATION

chimera 01_ parent for generation 6.1

3 8 4 81 96

0.82

4 4 8 6 8 4 54 24

0.57

12 12 8 4 8 12 27 24

0.56

8 4 12 2 16 8 27 48

0.70

12 4 8

Gabriel Ivorra Morell, Nicolรกs Villegas Giorgi, Nicolรกs Leguina de Enterria, Jens Pedersen

individual 08 I

individual 09 I

individual 10

individual 11

Emergent Technologies & Design 2010-2011

CHIMERA 1

SCALE

1.5

2.5

COPY

-1.5

1 2 1 2 1

MOVE SCALE MOVE SCALE SCALE

1.5 1.5 0 2.5 1.5

1.5 1.5 0 1.5 1.5

0 2.5 0 1.5 1.5

COPY

1.5

-1.5

1.5

SCALE

1.5

2.5

1.5

COPY

-1.5

1 2 1 1

MOVE COPY MOVE MOVE

0 0 2.5 0

-2.5 0 0 0 -2.5 -2.5 1.5 -1.5

COPY

-1.5

SCALE

2.5

1.5

COPY

1.5

SCALE

1.5

1 2 1 2 1 1 2 1 1 2 1

CHIMERA 1

COPY SCALE MOVE MOVE COPY SCALE MOVE SCALE MOVE COPY SCALE COPY

-1.5 2.5 -1.5 0 1.5 2.5 0 1.5 1.5 -1.5 2.5 0

-1.5 1.5 1.5 -1.5 -1.5 1.5 0 1.5 -1.5

0 1.5 -1.5 0 1.5 1.5 0 2.5 -1.5

1.5 2.5 0

0 1.5 0

COPY

1.5

-1.5

1.5

1 2 1

MOVE SCALE COPY

-1.5 1.5 1.5

1.5 1.5 0

-1.5 2.5 -1.5

1 2 1

MOVE COPY MOVE

0 0 2.5

-2.5 0 -2.5

0 0 -2.5

1 2 1

MOVE COPY MOVE

-2.5 2.5 2.5

0 0 -2.5

-2.5 -2.5 2.5

MOVE

-2.5

SCALE

individual 12

6 8

8 81 72

0.65

4 12

individual 13

4 16

0.76

individual 14

4 16

4 54 48

0.47

individual 15

4 24

8 27 48 8 4 8

0.34

CHIMERA 1

DEVELOPMENT (RESULT)

TYPE

MOVE

-2.5

2.5

SCALE

1.5

2.5

COPY

-1.5

1 2 1 2 1

MOVE SCALE MOVE SCALE SCALE

1.5 1.5 0 2.5 1.5

1.5 1.5 0 1.5 1.5

0 2.5 0 1.5 1.5

COPY

1.5

-1.5

1.5

SCALE

1.5

2.5

1.5

COPY

-1.5

2 1

SCALE MOVE

1.5 1.5

1.5 -1.5

2.5 -1.5

1 2 1

COPY SCALE COPY

-1.5 2.5 0

1.5 2.5 0

0 1.5 0

COPY

1.5

-1.5

1.5

1 2 1

MOVE SCALE COPY

-1.5 1.5 1.5

1.5 1.5 0

-1.5 2.5 -1.5

1 2 1

MOVE COPY MOVE

0 0 2.5

-2.5 0 -2.5

0 0 -2.5

1 2 1

MOVE COPY MOVE

-2.5 2.5 2.5

0 0 -2.5

-2.5 -2.5 2.5

MOVE

-2.5

SCALE

MOVE

-2.5

2.5

SCALE

1.5

2.5

COPY

-1.5

1 2 1 2 1

MOVE SCALE MOVE SCALE SCALE

1.5 1.5 0 2.5 1.5

1.5 1.5 0 1.5 1.5

0 2.5 0 1.5 1.5

COPY

1.5

-1.5

1.5

SCALE

1.5

2.5

1.5

COPY

-1.5

CC/CF

TRANSFORMATION

STEP

FROM

DEVELOPMENT

QUADRANT 3

4 8

1 2

BODY PLAN

INDIVIDUAL

GENERATION

CC/CF

DEVELOPMENT (RESULT)

TYPE 1

CHIMERA 1

TRANSFORMATION

STEP

FROM

DEVELOPMENT

4 8

1 2

QUADRANT

BODY PLAN

SEQUENCE 01 - GENERATION 06_part 02

INDIVIDUAL

GENERATION

EMERGENCE SEMINAR-GROUP 10

6 24 0 27 24

0.65

4 8 8 4 16 8 54 48

0.53

0 8 4 6 8 12 27 24

0.30

4 4 4 4 24 12 0 24

0.52

4 12 8

Gabriel Ivorra Morell, Nicolรกs Villegas Giorgi, Nicolรกs Leguina de Enterria, Jens Pedersen

EMERGENCE SEMINAR-GROUP 10

SEQUENCE 01 - GENERATION 06_part 02

individual 16

3 4

individual 17

3 4 1

12 4

individual 18

3 4

8 4

3 4

4 12

Emergent Technologies & Design 2010-2011

CHIMERA 2

DEVELOPMENT (RESULT)

TYPE MOVE COPY MOVE

X 0 0 2.5

Y -2.5 0 -2.5

Z 0 0 -2.5

1 2 1

MOVE COPY MOVE

-2.5 2.5 2.5

0 0 -2.5

-2.5 -2.5 2.5

MOVE

-2.5

SCALE

MOVE

-2.5

2.5

SCALE

1.5

2.5

COPY

-1.5

1 2 1 2 1

MOVE SCALE MOVE SCALE SCALE

1.5 1.5 0 2.5 1.5

1.5 1.5 0 1.5 1.5

0 2.5 0 1.5 1.5

COPY

1.5

-1.5

1.5

SCALE

1.5

2.5

1.5

COPY

-1.5

1 2 1

MOVE COPY MOVE

0 0 2.5

-2.5 0 -2.5

0 0 -2.5

1 2 1

MOVE COPY MOVE

-2.5 2.5 2.5

0 0 -2.5

-2.5 -2.5 2.5

MOVE

-2.5

SCALE

MOVE

-2.5

2.5

individual 20 6 8

0 0 16

0.43

individual 21

4 24

8 108 24

0.45

individual 22

0 8

8 72 24 12 4 24

0.44

CHIMERA 2

DEVELOPMENT (RESULT)

TYPE SCALE

X 1.5

Y 2.5

Z 2.5

COPY

-1.5

1 2 1 2 1

MOVE SCALE MOVE SCALE SCALE

1.5 1.5 0 2.5 1.5

1.5 1.5 0 1.5 1.5

0 2.5 0 1.5 1.5

COPY

1.5

-1.5

1.5

SCALE

1.5

2.5

1.5

COPY

-1.5

1 2 1

MOVE COPY MOVE

0 0 2.5

-2.5 0 -2.5

0 0 -2.5

1 2 1

MOVE COPY MOVE

-2.5 2.5 2.5

0 0 -2.5

-2.5 -2.5 2.5

MOVE

-2.5

SCALE

MOVE

-2.5

2.5

SCALE

1.5

2.5

COPY

-1.5

1 2 1 2 1

MOVE SCALE MOVE SCALE SCALE

1.5 1.5 0 2.5 1.5

1.5 1.5 0 1.5 1.5

0 2.5 0 1.5 1.5

COPY

1.5

-1.5

1.5

SCALE

1.5

2.5

1.5

COPY

-1.5

1 2 1

MOVE SCALE SCALE

-1 2 2

-1 2 1

0 2 1

1 2 1 2 1

MOVE SCALE MOVE SCALE COPY

-1 1 1 1 -1

-1 1 1 2 0

-1 1 0 1 0

1 2 1

MOVE SCALE COPY

0 2 1

0 1 1

-1 2 1

1 2

MOVE COPY

-1 1

1 1

-1 1

CC/CF

TRANSFORMATION

STEP

FROM

CC/CF

TRANSFORMATION

1 2 1

CHIMERA 2

STEP

FROM

DEVELOPMENT

QUADRANT

BODY PLAN

INDIVIDUAL

GENERATION

DEVELOPMENT

individual 19

QUADRANT

BODY PLAN

INDIVIDUAL

GENERATION

chimera 03_ parent for generation 6.2

2 8 4 72 24

0.49

36 4 36 2 16 8 0 16

0.50

24 6 36 4 24 4 36 16

0.40

12 4 24 6 8 8 0 16

0.69

12 4 24

Gabriel Ivorra Morell, Nicolรกs Villegas Giorgi, Nicolรกs Leguina de Enterria, Jens Pedersen

individual 23 I

individual 24 I

individual 25 I

individual 26

Emergent Technologies & Design 2010-2011

CHIMERA 2

TYPE MOVE COPY MOVE

X 0 0 2.5

Y -2.5 0 -2.5

Z 0 0 -2.5

1 2 1

MOVE COPY MOVE

-2.5 2.5 2.5

0 0 -2.5

-2.5 -2.5 2.5

MOVE

-2.5

SCALE

MOVE

-2.5

2.5

1 2 1

MOVE SCALE SCALE

-1 2 2

-1 2 1

0 2 1

1 2 1 2 1

MOVE SCALE MOVE SCALE COPY

-1 1 1 1 -1

-1 1 1 2 0

-1 1 0 1 0

1 2 1

MOVE SCALE COPY

0 2 1

0 1 1

-1 2 1

1 2 1 2 1

MOVE COPY MOVE COPY MOVE

-1 1 0 0 2.5

1 1 -2.5 0 -2.5

-1 1 0 0 -2.5

1 2 1

MOVE COPY MOVE

-2.5 2.5 2.5

0 0 -2.5

-2.5 -2.5 2.5

MOVE

-2.5

SCALE

MOVE

-2.5

2.5

1 2 1

MOVE COPY MOVE

0 0 2.5

-2.5 0 -2.5

0 0 -2.5

1 2 1

MOVE COPY MOVE

-2.5 2.5 2.5

0 0 -2.5

-2.5 -2.5 2.5

MOVE

-2.5

SCALE

MOVE

-2.5

2.5

individual 27

4 8

8 36 24

0.37

6 24

individual 28

0 16

0.31

individual 29

2 16

4 144 16

0.57

individual 30

8 16

8 72 24 24 2 12

0.80

CHIMERA 2

DEVELOPMENT (RESULT)

TYPE

SCALE

1.5

2.5

COPY

-1.5

1 2 1 2 1

MOVE SCALE MOVE SCALE SCALE

1.5 1.5 0 2.5 1.5

1.5 1.5 0 1.5 1.5

0 2.5 0 1.5 1.5

COPY

1.5

-1.5

1.5

SCALE

1.5

2.5

1.5

COPY

-1.5

1 2 1

MOVE SCALE SCALE

-1 2 2

-1 2 1

0 2 1

1 2 1 2 1

MOVE SCALE MOVE SCALE COPY

-1 1 1 1 -1

-1 1 1 2 0

-1 1 0 1 0

1 2 1

MOVE SCALE COPY

0 2 1

0 1 1

-1 2 1

1 2 1 2 1

MOVE COPY MOVE COPY MOVE

-1 1 0 0 2.5

1 1 -2.5 0 -2.5

-1 1 0 0 -2.5

1 2 1

MOVE COPY MOVE

-2.5 2.5 2.5

0 0 -2.5

-2.5 -2.5 2.5

MOVE

-2.5

SCALE

MOVE

-2.5

2.5

SCALE

1.5

2.5

COPY

-1.5

1 2 1 2 1

MOVE SCALE MOVE SCALE SCALE

1.5 1.5 0 2.5 1.5

1.5 1.5 0 1.5 1.5

0 2.5 0 1.5 1.5

COPY

1.5

-1.5

1.5

SCALE

1.5

2.5

1.5

COPY

-1.5

CC/CF

TRANSFORMATION

STEP

FROM

DEVELOPMENT

QUADRANT

BODY PLAN

INDIVIDUAL

GENERATION

DEVELOPMENT (RESULT)

1 2 1

CC/CF

TRANSFORMATION

STEP

FROM

DEVELOPMENT 8

QUADRANT

BODY PLAN

SEQUENCE 01 - GENERATION 06_part 02

INDIVIDUAL

GENERATION

EMERGENCE SEMINAR-GROUP 10

2 24 4 36 8

0.56

12 2 36 4 16 4 108 0

0.79

12 4 36 4 16 8 36 24

0.54

36 2 24 6 16 0 136 16

0.57

12 4 2

Gabriel Ivorra Morell, Nicolรกs Villegas Giorgi, Nicolรกs Leguina de Enterria, Jens Pedersen

EMERGENCE SEMINAR-GROUP 10

#13: ind. 15 ratio: 0.52

#14: ind. 13 ratio: 0.53

#15: ind. 29 ratio: 0.54

#16: ind. 03 ratio: 0.55

#17: ind. 06 ratio: 0.56

#18: ind. 27 ratio: 0.56

#19: ind. 30 ratio: 0.57

#20: ind. 05 ratio: 0.57

#21: ind. 25 ratio: 0.57

#22: ind. 12 ratio: 0.65

#23: ind. 08 ratio: 0.65

#24: ind. 22 ratio: 0.69

#25: ind. 07 ratio: 0.70

#26: ind. 09 ratio: 0.76

#27: ind. 28 ratio: 0.79

#28: ind. 26 ratio: 0.80

#29: ind. 04 ratio: 0.82

#30: ind. 02 ratio: 0.90

Emergent Technologies & Design 2010-2011

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

0.30 0.90 0.55 0.82 0.57 0.56 0.70 0.65 0.76 0.47 0.34 0.65 0.53 0.30 0.52 0.43 0.45 0.44 0.49 0.50 0.40 0.69 0.37 0.31 0.57 0.80 0.56 0.79 0.54 0.57

0.30 0.16 0.90 0.55 0.82 0.57 0.56 0.70 0.65 0.76 0.47 0.34 0.65 0.53 0.30 0.52 0.43 0.45 0.44 0.49 0.50 0.40 0.69 0.37 0.31 0.57 0.80 0.56 0.79 0.54 0.57

0.16 0.55

0.55 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

1 0.30 2 0.90 3 0.55 4 0.82 5 0.57 6 0.56 7 0.70 8 0.65 9 0.76 100.47 110.34 120.65 130.53 140.30 150.52 160.43 170.45 180.44 190.49 200.50 210.40 220.69 230.37 240.31 250.57 260.80 270.56 280.79 290.54 300.57

0.30 0.73 0.90 0.23 0.55 2.47 0.82 0.64 0.57 2.45 0.56 2.47 0.70 1.58 0.65 2.04 0.76 1.06 0.47 2.20 0.34 1.07 0.65 2.07 0.53 2.46 0.30 0.70 0.52 2.43 0.43 1.88 0.45 2.05 0.44 1.94 0.49 2.29 0.50 2.35 0.40 1.63 0.69 1.67 0.37 1.33 0.31 0.80 0.57 2.45 0.80 0.79 0.56 2.47 0.79 0.83 0.54 2.46 0.57 2.45

0.73 0.23 2.47 0.64 2.45 2.47 1.58 2.04 1.06 2.20 1.07 2.07 2.46 0.70 2.43 1.88 2.05 1.94 2.29 2.35 1.63 1.67 1.33 0.80 2.45 0.79 2.47 0.83 2.46 2.45

14 1 24 11 23 21 16 18 17 10 19 20 15 13 29 3 6 27 30 5 25 12 8 22 7 9 28 26 4 2

SORT

#12: ind. 20 ratio: 0.50

SORT

#11: ind. 19 ratio: 0.49

NORMAL DISTRIBUTION

#10: ind. 10 ratio: 0.47

RATIO COPY NORMAL DISTRIBUTION

#09: ind. 17 ratio: 0.45

IN RATIO COPY

#07: ind. 16 ratio: 0.43

AVERAGE IN

#06: ind. 21 ratio: 0.40

STANDAR DEVIATION AVERAGE

#05: ind. 23 ratio: 0.37

RATIO STANDAR DEVIATION

#04: ind. 11 ratio: 0.34

LONG RATIO

#03: ind. 24 ratio: 0.31

SHORT LONG

#02: ind. 01 ratio: 0.30

INDIVIDUAL SHORT

#01: ind. 14 ratio: 0.30

INDIVIDUAL

#08: ind. 18 ratio: 0.44

SEQUENCE 01 - GENERATION 06 parent of generation 07

generation 06 30 individuals’ fitness ranking

14 0.30 1 0.30 24 0.31 11 0.34 23 0.37 21 0.40 16 0.43 18 0.44 17 0.45 10 0.47 19 0.49 20 0.50 15 0.52 13 0.53 29 0.54 3 0.55 6 0.56 27 0.56 30 0.57 5 0.57 25 0.57 12 0.65 8 0.65 22 0.69 7 0.70 9 0.76 28 0.79 26 0.80 4 0.82 2 0.90

0.30 0.30 0.31 0.34 0.37 0.40 0.43 0.44 0.45 0.47 0.49 0.50 0.52 0.53 0.54 0.55 0.56 0.56 0.57 0.57 0.57 0.65 0.65 0.69 0.70 0.76 0.79 0.80 0.82 0.90

Gabriel Ivorra Morell, Nicolás Villegas Giorgi, Nicolás Leguina de Enterria, Jens Pedersen

EMERGENCE SEMINAR-GROUP 10

#01: ind. 14 ratio: 0.30

#02: ind. 01 ratio: 0.30

#03: ind. 24 ratio: 0.31

#04: ind. 11 ratio: 0.34

#05: ind. 23 ratio: 0.37

#06: ind. 21 ratio: 0.40

#07: ind. 16 ratio: 0.43

#09: ind. 17 ratio: 0.45

#10: ind. 10 ratio: 0.47

#11: ind. 19 ratio: 0.49

#12: ind. 20 ratio: 0.50

#13: ind. 15 ratio: 0.52

#14: ind. 13 ratio: 0.53

#15: ind. 29 ratio: 0.54

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

0.30 0.30 0.31 0.34 0.37 0.40 0.43 0.44 0.45 0.47 0.49 0.50 0.52 0.53 0.54

0.70 0.73 0.80 1.07 1.33 1.63 1.88 1.94 2.05 2.20 2.29 2.35 2.43 2.46 2.46

SORT

0.43

NORMAL DISTRIBUTION

0.09

RATIO COPY

normal distribution for 30 individuals

0.30 0.30 0.31 0.34 0.37 0.40 0.43 0.44 0.45 0.47 0.49 0.50 0.52 0.53 0.54

statistical simulation

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

AVERAGE

fitness evaluation

STANDAR DEVIATION

point distribution

INDIVIDUAL

individual ratio

RATIO

#08: ind. 18 ratio: 0.44

SEQUENCE 01 - GENERATION 06 parent of generation 07

generation 06 15 fittest individuals’ fitness ranking

14 1 24 11 23 21 16 18 17 10 19 20 15 13 29

0.30 0.30 0.31 0.34 0.37 0.40 0.43 0.44 0.45 0.47 0.49 0.50 0.52 0.53 0.54

Generation 6 is generated by a combination of genes of previous individuals. As a result the data suggest a more diverse population with average incidence and ratios. However the average value of the ratios is significantly lower than those displayed by previous generations. All this means that to obtain a diverse population the “superfitt” individuals disappear thus extreme characteristics are less likely to be pass on trough generations when sexual reproduction is involved.

Emergent Technologies & Design 2010-2011

Gabriel Ivorra Morell, Nicolás Villegas Giorgi, Nicolás Leguina de Enterria, Jens Pedersen

EMERGENCE SEMINAR-GROUP 10

sequence 01_evaluation

The approach of asexual reproduction and the passing of the fittest genomes had an immediate effect on the passing on of certain fit characteristics. Certain body parts started to accumulate instructions that made them fitter improving the fitness of the individual on the global scale, and thus the instructions (genome) for these body parts were passed from generation to generation. This becomes easily visible with the S3 (light green) which starts growing on the z direction and so ensures it gets passed to the following generations. This made certain characteristics common to all generations, and even thought there was an obvious variation it was clearly visible that all generations had common antecessors. Even after sexual reproduction the fit characteristics still survive since the resulting offspring that carry this genomes are the ones that “live” on to pass their code.

Emergent Technologies & Design 2010-2011

This results in a certain differentiated organisation of the body plan, where certain parts become “specialised” in increasing the fitness criteria since they accumulate the biggest number of instructions that make them fit, and thus keep passing down, and since they are always present have in turn more possibilities of adding more “useful” code to the previous one. The generations keep getting fitter and fitter as the sequence is carried on and their characteristics become more and more acute as the populations become more and more homogeneous, until a point is reached where the extreme homogeneity of the resulting population would, in a real natural environment, mean its own demise , since any minor change on the environmental pressure (fitness criteria) would wipe out the whole population due to the fact that every single individual is too specific to a given set of conditions. Once this point is reached it is decided that a mutation strategy should be initiated to increase the heterogeneity of the populations to ensure their survival even after a change in the fitness criteria; and thus sequence 2 is started.

Gabriel Ivorra Morell, Nicolás Villegas Giorgi, Nicolás Leguina de Enterria, Jens Pedersen

EMERGENCE SEMINAR-GROUP 10

sequence 02_introduction a+b

Inferior =

Superior =

a+b

Inferior + Superior

Ratio for sequence 02 & 03 =

h a

By now a considerable amount of genetic information had been created by an additive and iterative process of asexual reproduction combined with a crossbreeding strategy. The fitness had gradually gone up from 0.86 to 0.3 and the fittest individual had been selected. The following step in the evolutive process was the generation of heterogeneity by mutation of the genome of the fittest individual to ensure preservation of the next populations even after a change in the fitness criteria had been implemented. As we had observed certain specialisation of the body plan emerge from the previous iterative and additive process, we decided to set a new fitness criteria that would enforce such a differentiation. For such purpose we decided to apply two different criteria to two different parts of the body. For the superior body parts the fitness was kept the same the average of the length and width of the base of the bounding box divided by the height, taking the lowest as the fittest (again this would select the tall and slender superior body parts); but for the lower body parts the opposite fitness was applied the height of the bounding box, divided by the average between the length and the width of the base, making the lowest the fittest (meaning that the flatter the inferior body parts became the fitter they would be). The average of both fitness criteria was taken as the global fitness criteria which meant that for an individual to be fit it would need to have differentiated superior and inferior body parts. A new script was created, the only input for such script this time was the genome of the fittest individual. By doing this we were trying to prove that just by applying a set of random mutations (in this case translocations, duplications, and deletions), a great deal of heterogeneity could be generated even if the input was a genome selected to create a given fit individual; and that even with this great amount of mutations taken place each time, an evolutionary trend could be observed. The script was a mutation generator. The fittest genome was fed to the mutation generator in the following way. The given number of total transformations for each body part was introduced, the total amount of different types of transformations (coded as 0=scale, 1=move, and 2= copy), and the values

Emergent Technologies & Design 2010-2011

for such types, going from -2.5 to 2.5 for the moves and copies, and from 1 to 2 for the scales. The script shuffled all this information while maintaining an organised pattern so the values of copies, were not mixed up with the ones for scales or moves, and so on, and so the total number of transformations were respected. So what was in reality going on was that we were generating a enormous amount of translocation mutations, we were speeding up by means of computational tools the natural phenomena of mutations to see how this would affect our evolutionary process and too see how variation could be implemented into a given genome even if such genome had been selected due to evaluation criteria that were no longer applicable. For this sequence 3 populations were generated in the following fashion. The fittest genome of sequence 1 was inputted and the mutation generator outputted 15 individuals that were then ranked according to the new fitness criteria. The genome of the fittest was then fed to the mutation generator again to generate the next population, and so on. In contrary to the first sequence were the order of the selected genome to pass was always respected and the variation came from new genome being added to the previous one. Here no new genome was added (the number of total transformations was always the same) the variation came from the fact that the order of such transformations was never respected. But if the order was never respected and was always randomly generated for each population, how was an evolutionary behaviour going to arise? The answer to this was that even though the total number of transformations were respected, and the values for the transformations were not mixed between transformations, the ratio of transformations could vary. So for a given amount of transformations the percentage for each type could vary, having the “favourable” type a bigger chance of occurring after each generation since it became more and more present in the input genome. The same applied for the values, even though they were not shuffled between transformation types if bigger values on the Z axis were more favourable their presence in the total amount of values would increase gradually as they were fed as part of the input genome each time. In practice what was taking place was a great deal of translocation mutations, accompanied by duplication and deletion as some instructions became more present than others.

Gabriel Ivorra Morell, Nicolás Villegas Giorgi, Nicolás Leguina de Enterria, Jens Pedersen

individual 03 I

individual 04 I

0.96

F(SMC)=S16(0)+M26(1)+C13(2)

1 4

individual 06 I

1.13

F(SMC)=S12(0)+M35(1)+C8(2)

2 4

individual 07 I

0.88

individual 08 I

1 F(SMC)=S14(0)+M32(1)+C9(2)

2 4 1

0.85

DEVELOPMENT (RESULT)

F(SMC)=S13(0)+M28(1)+C12(2)

CC/CF

TRANSFORMATION

STEP

FROM

DEVELOPMENT

QUADRANT

S M C

BODY PLAN

INDIVIDUAL

0 1 2

UNIFIED GENOME

GENERATION

DEVELOPMENT (RESULT)

Emergent Technologies & Design 2010-2011

CC/CF

TRANSFORMATION

STEP

UNIFIED GENOME

FROM

DEVELOPMENT

= = =

UNIFIED GENOME

SCALE MOVE COPY

UNIFIED GENOME

F(SMC)=S16(0)+M33(1)+C13(2)

individual 05

UNIFIED GENOME

individual 02

S M C

0 1 2

UNIFIED GENOME

= = =

UNIFIED GENOME

SCALE MOVE COPY

UNIFIED GENOME

individual 01

QUADRANT

BODY PLAN

SEQUENCE 02 - GENERATION 07

INDIVIDUAL

GENERATION

EMERGENCE SEMINAR-GROUP 10

4 2

0.85

F(SMC)=S13(0)+M28(1)+C12(2)

1 1

0.69

F(SMC)=S13(0)+M28(1)+C12(2)

4 2

1.76

1 F(SMC)=S21(0)+M30(1)+C4(2)

1 1

0.91

Gabriel Ivorra Morell, Nicolรกs Villegas Giorgi, Nicolรกs Leguina de Enterria, Jens Pedersen

individual 11 I

UNIFIED GENOME

individual 14

2 2

F(SMC)=S11(0)+M33(1)+C12(2)

0.63

4 F(SMC)=S13(0)+M29(1)+C14(2)

1 2

1 F(SMC)=S17(0)+M29(1)+C9(2)

2 16

4 1

F(SMC)=S17(0)+M27(1)+C11(2)

1.26

1 32

Emergent Technologies & Design 2010-2011

F(SMC)=S14(0)+M27(1)+C14(2)

8 1

0.72

individual 15

0.88

individual 12

1.27

UNIFIED GENOME

individual 10

DEVELOPMENT (RESULT)

127

CC/CF

UNIFIED GENOME

F(SMC)=S25(0)+M20(1)+C12(2)

TRANSFORMATION

STEP

UNIFIED GENOME

FROM

UNIFIED GENOME

TYPE

individual 13

UNIFIED GENOME

DEVELOPMENT

QUADRANT

BODY PLAN

INDIVIDUAL

GENERATION

UNIFIED GENOME

DEVELOPMENT (RESULT) 2

TYPE

individual 09

CC/CF

TRANSFORMATION

STEP

DEVELOPMENT

FROM

QUADRANT

BODY PLAN

SEQUENCE 02 - GENERATION 07

INDIVIDUAL

GENERATION

EMERGENCE SEMINAR-GROUP 10

F(SMC)=S16(0)+M25(1)+C14(2)

4 4

0.54

0.93

Gabriel Ivorra Morell, Nicolรกs Villegas Giorgi, Nicolรกs Leguina de Enterria, Jens Pedersen

EMERGENCE SEMINAR-GROUP 10

#01: ind. 15 ratio: 0.54

#02: ind. 10 ratio: 0.63

#03: ind. 06 ratio: 0.69

#04: ind. 14 ratio: 0.72

#05: ind. 04 ratio: 0.85

#06: ind. 05 ratio: 0.85

#07: ind. 13 ratio: 0.88

#09: ind. 08 ratio: 0.91

#10: ind. 12 ratio: 0.93

#11: ind. 01 ratio: 0.96

#12: ind. 02 ratio: 1.13

#13: ind. 11 ratio: 1.26

#14: ind. 09 ratio: 1.27

#15: ind. 07 ratio: 1.76

NORMAL DISTRIBUTION

0.96 0.11 1.02 1.13 0.88 0.85 0.85 0.69 1.76 0.91 1.27 0.63 1.26 0.93 0.88 0.72 0.54

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

0.96 1.13 0.88 0.85 0.85 0.69 1.76 0.91 1.27 0.63 1.26 0.93 0.88 0.72 0.54

1.30 1.09 1.27 1.24 1.24 0.91 0.04 1.29 0.75 0.74 0.79 1.30 1.27 0.98 1.21

SORT

RATIO COPY

normal distribution for 15 individuals

statistical simulation

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

AVERAGE

fitness evaluation

STANDAR DEVIATION

point distribution

INDIVIDUAL

individual ratio

RATIO

#08: ind. 03 ratio: 0.88

SEQUENCE 02 - GENERATION 07 parent of generation 08

generation 07 fitness ranking

15 10 6 14 4 5 13 3 8 12 1 2 11 9 7

0.54 0.63 0.69 0.72 0.85 0.85 0.88 0.88 0.91 0.93 0.96 1.13 1.26 1.27 1.76

The new criteria used to evaluate this generation could be interpreted as a change in the environment. The data obtain from this population is then not comparable to data from previous generations. Here, the graphs show a set of fairly homogenous individuals with the tendency to produce extremely “fit” individuals every now end then. However the relevance of this particular approach can only be understood in comparison with data from future generations.

Emergent Technologies & Design 2010-2011

Gabriel Ivorra Morell, Nicolás Villegas Giorgi, Nicolás Leguina de Enterria, Jens Pedersen

individual 03 I

individual 04 I

1.06

1 1

F(SMC)=S15(0)+M23(1)+C18(2)

1 8

individual 06 I

0.62

F(SMC)=S11(0)+M28(1)+C16(2)

individual 07 I

0.85

F(SMC)=S14(0)+M32(1)+C16(2)

individual 08 I

0.31

= = =

0 1 2

DEVELOPMENT (RESULT) S M C

CC/CF

TRANSFORMATION

STEP

FROM

DEVELOPMENT

QUADRANT

2 1 1

F(SMC)=S17(0)+M31(1)+C8(2)

8 8

0.81

F(SMC)=S8(0)+M22(1)+C12(2)

2 4

0.98

F(SMC)=S15(0)+M31(1)+C8(2)

4 2

0.77

BODY PLAN

INDIVIDUAL

UNIFIED GENOME

GENERATION

DEVELOPMENT (RESULT)

Emergent Technologies & Design 2010-2011

CC/CF

TRANSFORMATION

STEP

UNIFIED GENOME

FROM

DEVELOPMENT

UNIFIED GENOME

SCALE MOVE COPY

UNIFIED GENOME

F(SMC)=S16(0)+M21(1)+C(2)

individual 05

UNIFIED GENOME

individual 02

S M C

0 1 2

UNIFIED GENOME

= = =

UNIFIED GENOME

SCALE MOVE COPY

UNIFIED GENOME

individual 01

QUADRANT

BODY PLAN

SEQUENCE 02 - GENERATION 08

INDIVIDUAL

GENERATION

EMERGENCE SEMINAR-GROUP 10

8 F(SMC)=S16(0)+M20(1)+C18(2)

2 1

0.68

Gabriel Ivorra Morell, Nicolรกs Villegas Giorgi, Nicolรกs Leguina de Enterria, Jens Pedersen

individual 11 I

individual 12 I

DEVELOPMENT (RESULT)

0.70

individual 14 I

2.00

individual 15 I

4 8 4

0.68

1 2

UNIFIED GENOME

1 F(SMC)=S12(0)+M32(1)+C9(2)

1 1

F(SMC)=S17(0)+M29(1)+C32(2)

4 2

F(SMC)=S12(0)+M32(1)+C16(2)

8 32

Emergent Technologies & Design 2010-2011

F(SMC)=S18(0)+M20(1)+C17(2)

8 16

1.52

1.44

CC/CF

TRANSFORMATION

STEP

UNIFIED GENOME

FROM

F(SMC)=S13(0)+M24(1)+C17(2)

DEVELOPMENT 1

QUADRANT 2

BODY PLAN

INDIVIDUAL

GENERATION

TRANSFORMATION

STEP

FROM

DEVELOPMENT

DEVELOPMENT (RESULT)

CC/CF

S M C

F(SMC)=S18(0)+M24(1)+C12(2)

0 1 2

= = =

UNIFIED GENOME

SCALE MOVE COPY

F(SMC)=S16(0)+M27(1)+C12(2)

individual 13

UNIFIED GENOME

individual 10

S M C

0 1 2

UNIFIED GENOME

= = =

UNIFIED GENOME

SCALE MOVE COPY

UNIFIED GENOME

individual 09

QUADRANT

BODY PLAN

SEQUENCE 02 - GENERATION 08

INDIVIDUAL

GENERATION

EMERGENCE SEMINAR-GROUP 10

0.58

1.01

Gabriel Ivorra Morell, Nicolรกs Villegas Giorgi, Nicolรกs Leguina de Enterria, Jens Pedersen

EMERGENCE SEMINAR-GROUP 10

#02: ind. 15 ratio: 0.58

#03: ind. 02 ratio: 0.62

#04: ind. 11 ratio: 0.68

#05: ind. 08 ratio: 0.68

#06: ind. 09 ratio: 0.70

#07: ind. 07 ratio: 0.77

#09: ind. 03 ratio: 0.85

#10: ind. 06 ratio: 0.98

#11: ind. 12 ratio: 1.01

#12: ind. 01 ratio: 1.06

#13: ind. 13 ratio: 1.44

#14: ind. 14 ratio: 1.52

#15: ind. 10 ratio: 2.00

statistical simulation

normal distribution for 15 individuals

SORT

NORMAL DISTRIBUTION

RATIO COPY

AVERAGE

fitness evaluation

STANDAR DEVIATION

point distribution

INDIVIDUAL

individual ratio

breeding parent 02_generation10

#01: ind. 04 ratio: 0.31

RATIO

#08: ind. 05 ratio: 0.81

SEQUENCE 02 - GENERATION 08 parent of generation 09

generation 08 fitness ranking

1 1.06 0.43 0.93 1 1.06 0.89 4 0.31 2 0.62 2 0.62 0.71 15 0.58 3 0.85 3 0.85 0.91 2 0.62 4 0.31 4 0.31 0.33 11 0.68 5 0.81 5 0.81 0.88 8 0.68 6 0.98 6 0.98 0.92 9 0.70 7 0.77 7 0.77 0.86 7 0.77 8 0.68 8 0.68 0.78 5 0.81 9 0.70 9 0.70 0.79 3 0.85 10 2.00 10 2.00 0.04 6 0.98 11 0.68 11 0.68 0.77 12 1.01 12 1.01 12 1.01 0.91 1 1.06 13 1.44 13 1.44 0.46 13 1.44 14 1.52 14 1.52 0.37 14 1.52 15 0.58 15 0.58 0.66 10 2.00 By analysing the Gaussian bell it can be infer that this population exhibits a wider range of ratios than the previous one. This is the result of the “refinement” of the genome to adapt to the new environment.

Individuals from this population however have very high ratio values due to the specialization of body parts that the algorithm encourages.

Emergent Technologies & Design 2010-2011

Gabriel Ivorra Morell, Nicolás Villegas Giorgi, Nicolás Leguina de Enterria, Jens Pedersen

individual 03 I

individual 04 I

F(SMC)=S18(0)+M20(1)+C18(2)

2 4

individual 06 I

0.34

individual 07 I

1 F(SMC)=S15(0)+M22(1)+16C(2)

1 1

0.48

1 1

F(SMC)=S17(0)+M32(1)+C15(2)

1 8 4

individual 08 I

0.37

DEVELOPMENT (RESULT)

CC/CF

TRANSFORMATION

STEP

2 2 8

F(SMC)=S13(0)+M16(1)+C19(2)

8 8

0.94

1 F(SMC)=S12(0)+M23(1)+C19(2)

16 8

0.86

FROM

DEVELOPMENT

QUADRANT 3

BODY PLAN

INDIVIDUAL

S M C

Emergent Technologies & Design 2010-2011

0.38

0 1 2

UNIFIED GENOME

= = =

UNIFIED GENOME

GENERATION

DEVELOPMENT (RESULT)

CC/CF

TRANSFORMATION

STEP

UNIFIED GENOME

FROM

DEVELOPMENT 1

F(SMC)=S18(0)+M23(1)+13C(2)

SCALE MOVE COPY

UNIFIED GENOME

individual 05

UNIFIED GENOME

individual 02

S M C

0 1 2

UNIFIED GENOME

= = =

UNIFIED GENOME

SCALE MOVE COPY

UNIFIED GENOME

individual 01

QUADRANT

BODY PLAN

SEQUENCE 02 - GENERATION 09

INDIVIDUAL

GENERATION

EMERGENCE SEMINAR-GROUP 10

1 F(SMC)=S17(0)+M31(1)+C12(2)

32 8

0.16

F(SMC)=S13(0)+M32(1)+C13(2)

4 8

0.47

Gabriel Ivorra Morell, Nicolรกs Villegas Giorgi, Nicolรกs Leguina de Enterria, Jens Pedersen

127

individual 11 I

individual 12 I

F(SMC)=S13(0)+M28(1)+C09(2)

F(SMC)=S25(0)+M20(1)+C12(2)

127

individual 14 I

0.24

individual 15 I

8 F(SMC)=S12(0)+M32(1)+C9(2)

4 2

2 F(SMC)=S17(0)+M29(1)+C32(2)

2 16

F(SMC)=S18(0)+M20(1)+C17(2)

15 32

Emergent Technologies & Design 2010-2011

0.61

0.14

0.58

F(SMC)=S13(0)+M24(1)+C17(2)

STEP

0.45

FROM

BODY PLAN

INDIVIDUAL

GENERATION

UNIFIED GENOME

TYPE

UNIFIED GENOME

individual 10

individual 13

UNIFIED GENOME

DEVELOPMENT (RESULT)

CC/CF

TRANSFORMATION

DEVELOPMENT

QUADRANT

UNIFIED GENOME

DEVELOPMENT (RESULT) 4

TYPE

individual 09

CC/CF

TRANSFORMATION

STEP

DEVELOPMENT

FROM

QUADRANT

BODY PLAN

SEQUENCE 02 - GENERATION 09

INDIVIDUAL

GENERATION

EMERGENCE SEMINAR-GROUP 10

F(SMC)=S25(0)+M20(1)+C12(2)

0.30

0.61

Gabriel Ivorra Morell, Nicolรกs Villegas Giorgi, Nicolรกs Leguina de Enterria, Jens Pedersen

EMERGENCE SEMINAR-GROUP 10

#05: ind. 02 ratio: 0.34

#06: ind. 04 ratio: 0.37

#07: ind. 01 ratio: 0.38

#10: ind. 09 ratio: 0.47

#11: ind. 03 ratio: 0.48

#12: ind. 13 ratio: 0.58

#13: ind. 12 ratio: 0.61

#14: ind. 06 ratio: 0.86

#15: ind. 05 ratio: 0.94

statistical simulation

Emergent Technologies & Design 2010-2011

normal distribution for 15 individuals

SORT

NORMAL DISTRIBUTION

RATIO COPY

fitness evaluation

point distribution

INDIVIDUAL

individual ratio

#04: ind. 15 ratio: 0.30

AVERAGE

#09: ind. 10 ratio: 0.45

#03: ind. 11 ratio: 0.24

STANDAR DEVIATION

#08: ind. 08 ratio: 0.44

#02: ind. 07 ratio: 0.16

RATIO

#01: ind. 14 ratio: 0.14

SEQUENCE 02 - GENERATION 09 breeding parent 01_generation10

generation 09 fitness ranking

1 0.38 0.23 0.45 1 0.38 1.68 14 0.14 2 0.34 2 0.34 1.57 7 0.16 3 0.48 3 0.48 1.75 11 0.24 4 0.37 4 0.37 1.66 15 0.30 5 0.94 5 0.94 0.17 2 0.34 6 0.86 6 0.86 0.35 4 0.37 7 0.16 7 0.16 0.77 1 0.38 8 0.44 8 0.44 1.76 8 0.44 9 0.47 9 0.47 1.75 10 0.45 10 0.45 10 0.45 1.76 9 0.47 11 0.24 11 0.24 1.15 3 0.48 12 0.61 12 0.61 1.39 13 0.58 13 0.58 13 0.58 1.50 12 0.61 14 0.14 14 0.14 0.70 6 0.86 15 0.30 15 0.30 1.41 5 0.94 The tendency of reducing the average ratio value observed from generation 07 to generation 08 does not carries on trough generation 09 however the Gaussian bell implies more dispersion of the data than previous generations. It is also interesting to see that in the statistical simulation the lower tail of the bell is below zero on the X axis. This means that even though the behaviour of the population can be simulated by this method, highly unfit individuals cannot be fiscally obtain because all ratios are always positive values.

Gabriel Ivorra Morell, Nicolรกs Villegas Giorgi, Nicolรกs Leguina de Enterria, Jens Pedersen

EMERGENCE SEMINAR-GROUP 10

sequence 02_evaluation

The approach of asexual reproduction with variation induced by an extremely high mutation rate was very successful in generating heterogeneity in the new populations even though they originated from a very narrow gene code, selected according to a criteria that was not fully applicable any more. It was extremely interesting to observe that even though mutations were occurring at such a rate certain characteristics from the fittest individual from sequence 1 kept appearing time and time again. This is noticeable again on the S3 body part, this is due to the fact that since the fitness criteria for the superior body part was the same as the fitness for sequence 1 and this body part in particular had been passed from generation to generation according to its good compliance of such criteria it kept appearing as it grew in the Z axis in this sequence. It becomes extremely apparent from the fact that the fittest individual in this population has a very visible S3 body part.

Emergent Technologies & Design 2010-2011

Another problem that arose came from the capacity of mutations to generate monsters where a whole body part absorbed all the rest, or the body “exploded”. This was due to the fact that the order of the transformations is extremely important and if, for example, the first move values are of 2.5 when the in the first steps the boxes only measure 1, the whole body will become disaggregated and any further scales or transformations will only increase such problem unless coincidentally they go in the exact opposite direction. Due to this fact we had to generate a complementary killing strategy that automatically got rid of such unsustainable monsters. On the general trend this sequence was successful since once more we could observe evolution as the populations got fitter and fitter, and again this happened in a similar fashion to sequence 1 where the fitness range and variation at first increased but then started to get more and more homogeneous. We had successfully generated an evolutionary process that again was generating too homogeneous and it populations.

The hierarchical differentiation of superior and inferior body parts was also noticeable. As the sequence ran the populations got fitter and fitter which meant a differentiation in the different body parts which is actually visually no- The next step to take with the aim of once more increasing the heterogeneity ticeable. On the downside of this differentiation was the fact that on the fittest of the populations was to start a crossbreeding strategy between fit and unfit individuals it wasn’t the whole lower and upper body part that were getting individuals. fitter, what tended to happen was that a certain body part of the superior or inferior part got extremely fit while the rest simply didn’t get unfit. This again is quite noticeable when observing the fittest individual of the sequence, where S3 and I3 account for the fitness, while the rest of body parts are hardly visible or have become embedded in the bigger parts. Gabriel Ivorra Morell, Nicolás Villegas Giorgi, Nicolás Leguina de Enterria, Jens Pedersen 41

EMERGENCE SEMINAR-GROUP 10

sequence 03_introduction

For this last sequence the increment in, both the variation, and the fitness range was going to be a consequence of the application of a crossbreeding strategy that bred the fittest individual of sequence 2 with the unfitest, while maintaining the mutation strategy. In order to be able to implement such a strategy a modification in the mutation code was carried out. The new inputs were now two genomes a fit and an unfit one, both maintaining the body plan differentiation. The script works in the following way; as before it maintains the total number of transformations, the types and the values for each type, shuffling the order in which they occur. To have into account the cross breeding without duplicating the genetic code it chooses one instruction from each parent at a time until it has completed the total amount of transformation present in the original genome. In effect it is working as a computational version of meiosis halving each genome so they can be coupled while avoiding a constant duplication of the genetic information. This script is ran to generate a first population which in turn is ranked to obtain once more the fittest and unfitest individuals whose genome is then feed to the script once more to obtain a second population. The expectations over this process were to increase the variation while still maintaining certain characteristics within the individuals. Emergent Technologies & Design 2010-2011

Gabriel Ivorra Morell, Nicolรกs Villegas Giorgi, Nicolรกs Leguina de Enterria, Jens Pedersen

individual 03 I

individual 04 I

Emergent Technologies & Design 2010-2011

0.48

F(SMC)=S13(0)+M24(1)+C17(2)

16 32

individual 06 I

0.80

F(SMC)=S25(0)+M20(1)+C12(2)

individual 07 I

0.42

F(SMC)=S25(0)+M20(1)+C12(2)

1 8 4

individual 08 I

2.99

DEVELOPMENT (RESULT)

CC/CF

TRANSFORMATION

STEP

2 8 F(SMC)=S13(0)+M16(1)+C19(2)

1 16

0.79

2 F(SMC)=S20(0)+M22(1)+C6(2)

1 32

0.68

FROM

DEVELOPMENT

QUADRANT 3

BODY PLAN

INDIVIDUAL

S M C

0 1 2

UNIFIED GENOME

= = =

UNIFIED GENOME

GENERATION

DEVELOPMENT (RESULT)

CC/CF

TRANSFORMATION

STEP

UNIFIED GENOME

FROM

DEVELOPMENT 1

F(SMC)=S25(0)+M20(1)+C12(2)

SCALE MOVE COPY

UNIFIED GENOME

individual 05

UNIFIED GENOME

individual 02

S M C

0 1 2

UNIFIED GENOME

= = =

UNIFIED GENOME

SCALE MOVE COPY

UNIFIED GENOME

individual 01

QUADRANT

BODY PLAN

SEQUENCE 03 - GENERATION 10

INDIVIDUAL

GENERATION

EMERGENCE SEMINAR-GROUP 10

1 F(SMC)=S18(0)+M20(1)+C18(2)

32 8

0.93

F(SMC)=S15(0)+M22(1)+16C(2)

4 8

0.75

Gabriel Ivorra Morell, Nicolรกs Villegas Giorgi, Nicolรกs Leguina de Enterria, Jens Pedersen

10 10

individual 11 I

10 11

individual 12 I

10 12

F(SMC)=S25(0)+M20(1)+C12(2)

10 13

F(SMC)=S25(0)+M20(1)+C12(2)

individual 14 I

10 14

0.94

F(SMC)=S13(0)+M24(1)+C17(2)

8 16

individual 15 I

1.14

8 F(SMC)=S25(0)+M20(1)+C12(2)

4 2

F(SMC)=S13(0)+M24(1)+C17(2)

2 16

F(SMC)=S25(0)+M20(1)+C12(2)

8 8

1 1

15 32

0.57

1.68

F(SMC)=S25(0)+M20(1)+C12(2)

10 15

Emergent Technologies & Design 2010-2011

0.35

STEP

FROM

BODY PLAN

INDIVIDUAL

GENERATION

1 UNIFIED GENOME

UNIFIED GENOME

TYPE

UNIFIED GENOME

individual 10

individual 13

UNIFIED GENOME

DEVELOPMENT (RESULT)

CC/CF

TRANSFORMATION

DEVELOPMENT

QUADRANT

UNIFIED GENOME

DEVELOPMENT (RESULT) 4

TYPE

individual 09

CC/CF

TRANSFORMATION

STEP

DEVELOPMENT

FROM

QUADRANT

BODY PLAN

SEQUENCE 03 - GENERATION 10

INDIVIDUAL

GENERATION

EMERGENCE SEMINAR-GROUP 10

1.40

0.88

Gabriel Ivorra Morell, Nicolรกs Villegas Giorgi, Nicolรกs Leguina de Enterria, Jens Pedersen

EMERGENCE SEMINAR-GROUP 10

#06: ind. 08 ratio: 0.75

#07: ind. 05 ratio: 0.79

#10: ind. 07 ratio: 0.93

#11: ind. 10 ratio: 0.94

#12: ind. 11 ratio: 1.14

#13: ind. 15 ratio: 1.40

#14: ind. 13 ratio: 1.68

#15: ind. 04 ratio: 2.99

normal distribution for 15 individuals

0.48 0.66 0.99 0.80 0.42 2.99 0.79 0.68 0.93 0.75 0.35 0.94 1.14 0.88 1.68 0.57 1.40

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

0.48 0.80 0.42 2.99 0.79 0.68 0.93 0.75 0.35 0.94 1.14 0.88 1.68 0.57 1.40

0.45 0.58 0.42 0.01 0.58 0.54 0.60 0.57 0.38 0.60 0.59 0.60 0.35 0.49 0.50

SORT

NORMAL DISTRIBUTION

statistical simulation

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

RATIO COPY

fitness evaluation

point distribution

breeding parent 02_generation11

#05: ind. 06 ratio: 0.68

INDIVIDUAL

individual ratio

#04: ind. 14 ratio: 0.57

AVERAGE

#09: ind. 12 ratio: 0.88

#03: ind. 01 ratio: 0.48

STANDAR DEVIATION

#08: ind. 02 ratio: 0.80

#02: ind. 03 ratio: 0.42

RATIO

#01: ind. 09 ratio: 0.35

SEQUENCE 03 - GENERATION 10 breeding parent 01_generation11

generation 10 fitness ranking

9 3 1 14 6 8 5 2 12 7 10 11 15 13 4

0.35 0.42 0.48 0.57 0.68 0.75 0.79 0.80 0.88 0.93 0.94 1.14 1.40 1.68 2.99

As a result of the breeding strategy, population 10 presents higher ratio values than its two predecessors. However, it exhibits more dispersion in the data. This means a very diverse population. The lower tail of the Gaussian bell still presents negative values.

Emergent Technologies & Design 2010-2011

Gabriel Ivorra Morell, Nicolรกs Villegas Giorgi, Nicolรกs Leguina de Enterria, Jens Pedersen

individual 03 I

individual 04 I

Emergent Technologies & Design 2010-2011

F(SMC)=S13(0)+M28(1)+C12(2)

1.98

individual 06 I

3.01

individual 07 I

2.40

4 8

individual 08 I

1.22

8 8

CC/CF

DEVELOPMENT (RESULT)

TRANSFORMATION

STEP

FROM

F(SMC)=S17(0)+M29(1)+C9(2)

DEVELOPMENT

F(SMC)=S13(0)+M29(1)+C14(2)

QUADRANT

BODY PLAN

INDIVIDUAL

GENERATION

CC/CF

F(SMC)=S21(0)+M30(1)+C4(2)

S M C

UNIFIED GENOME

individual 05

UNIFIED GENOME

4 UNIFIED GENOME

SCALE = 0 MOVE = 1 COPY = 2

UNIFIED GENOME

individual 02

UNIFIED GENOME

S M C

UNIFIED GENOME

SCALE = 0 MOVE = 1 COPY = 2

individual 01

DEVELOPMENT (RESULT)

TRANSFORMATION

STEP

FROM

DEVELOPMENT

QUADRANT

BODY PLAN

SEQUENCE 03 - GENERATION 11

INDIVIDUAL

GENERATION

EMERGENCE SEMINAR-GROUP 10

F(SMC)=S13(0)+M28(1)+C12(2)

1.37

2 8

F(SMC)=S13(0)+M28(1)+C12(2)

0.76

1 4

F(SMC)=S16(0)+M33(1)+C13(2)

2.21

2 2

F(SMC)=S14(0)+M32(1)+C9(2)

1.22

1 6

Gabriel Ivorra Morell, Nicolรกs Villegas Giorgi, Nicolรกs Leguina de Enterria, Jens Pedersen

individual 10 I

11 10

individual 11 I

11 11

individual 12 I

11 12

11 13

1.78

individual 14 I

11 14

8.26

individual 15 I

1 1

3.79

11 15

F(SMC)=S13(0)+M29(1)+C14(2)

F(SMC)=S17(0)+M29(1)+C9(2)

F(SMC)=S16(0)+M25(1)+C14(2)

1 1

1.28

3.35

0.95

CC/CF

DEVELOPMENT (RESULT)

TRANSFORMATION

STEP

FROM

F(SMC)=S14(0)+M27(1)+C14(2)

DEVELOPMENT

F(SMC)=S18(0)+M20(1)+C18(2)

QUADRANT

BODY PLAN

INDIVIDUAL

GENERATION

CC/CF

F(SMC)=S16(0)+M25(1)+C14(2)

S M C

Emergent Technologies & Design 2010-2011

individual 13

UNIFIED GENOME

F(SMC)=S17(0)+M29(1)+C9(2)

SCALE = 0 MOVE = 1 COPY = 2

UNIFIED GENOME

S M C

UNIFIED GENOME

SCALE = 0 MOVE = 1 COPY = 2

individual 09

DEVELOPMENT (RESULT)

TRANSFORMATION

STEP

FROM

DEVELOPMENT

QUADRANT

BODY PLAN

SEQUENCE 03 - GENERATION 11

INDIVIDUAL

GENERATION

EMERGENCE SEMINAR-GROUP 10

2.32

8 1

Gabriel Ivorra Morell, Nicolรกs Villegas Giorgi, Nicolรกs Leguina de Enterria, Jens Pedersen

EMERGENCE SEMINAR-GROUP 10

SEQUENCE 03 - GENERATION 11

generation 11 fitness ranking

#02: ind. 13 ratio: 0.95

#03: ind. 08 ratio: 1.22

#04: ind. 04 ratio: 1.22

#05: ind. 15 ratio: 1.28

#06: ind. 05 ratio: 1.37

#07: ind. 09 ratio: 1.78

#09: ind. 07 ratio: 2.21

#10: ind. 12 ratio: 2.32

#11: ind. 03 ratio: 2.40

#12: ind. 02 ratio: 3.01

#13: ind. 14 ratio: 3.35

#14: ind. 11 ratio: 3.79

#15: ind. 10 ratio: 8.26

NORMAL DISTRIBUTION

1.98 1.85 2.39 3.01 2.40 1.22 1.37 0.76 2.21 1.22 1.78 8.26 3.79 2.32 0.95 3.35 1.28

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1.98 3.01 2.40 1.22 1.37 0.76 2.21 1.22 1.78 8.26 3.79 2.32 0.95 3.35 1.28

0.21 0.20 0.22 0.18 0.18 0.15 0.21 0.18 0.20 0.00 0.16 0.22 0.16 0.19 0.18

SORT

RATIO COPY

normal distribution for 15 individuals

statistical simulation

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

AVERAGE

fitness evaluation

STANDAR DEVIATION

point distribution

INDIVIDUAL

individual ratio

RATIO

#08: ind. 01 ratio: 1.98

#01: ind. 06 ratio: 0.76

6 13 8 4 15 5 9 1 7 12 3 2 14 11 10

0.76 0.95 1.22 1.22 1.28 1.37 1.78 1.98 2.21 2.32 2.40 3.01 3.35 3.79 8.26

This population has the highest dispersion of data observed in the experiment. However this is not evident in the point distribution graph were all the individuals show a very close relation, except from one “extremely fit” individual.

Emergent Technologies & Design 2010-2011

Gabriel Ivorra Morell, Nicolás Villegas Giorgi, Nicolás Leguina de Enterria, Jens Pedersen

EMERGENCE SEMINAR-GROUP 10

sequence 03_evaluation

This approach that included sexual reproduction while maintaining an elevated mutation rate was extremely successful in generating an extremely heterogeneous population. It seems quite anti-intuitive that with such a range of variation within the output genome of the script very fit individuals still where able to come into existence at the same time as extremely unfit ones where generated. The unfitness ratio of individuals rose to unprecedented levels generating extreme monsters which in turn stretched the fitness range from a maximum of around 2 to 8. It seemed that by applying such variation generating forces as sexual reproduction, and an extremely high mutation rate, the population generative process started to spiral out of control. This potential for heterogeneity seems an extremely important part of the natural world, since it is able to stretch the range of variation to such an extent that even with any drastic change in the environmental pressures the survival of some individuals is always ensured. This is why in certain situations where the environmental pressure rises on a given population so does the mutation rate, speeding up the possibility for extreme individuals to arise that will be “fit” enough to survive. Emergent Technologies & Design 2010-2011

Another trend that could be observed was the fact that even after such a variation generative process was implemented, certain characteristics present in the original input genome kept arising sometimes in extreme fashion. It is visible that since the original genome was selected due to the fact that it generated long and slender individuals this potential was still present in the genetic code and had a higher tendency to emerge from the mutational and interbreeding generator than any other spatial arrangement. The appearance of such spatial configuration seems to be more present in the extreme individuals, a trend, easily noticeable in the fact that both the fittest and unfitest of the final population would be extremely fit if the fitness criteria for the first sequence was still being applied. At the same time the individuals in the middle of the bell curve show a more balanced body plan where no parts are extremely slender or flat.

Gabriel Ivorra Morell, Nicolás Villegas Giorgi, Nicolás Leguina de Enterria, Jens Pedersen

EMERGENCE SEMINAR-GROUP 10

GENERATION’S SUMMARY

generation generation generation generation generation 01 02 03 04 05

chimeras

generation generation generation generation generation generation generation generation 06.1 06.2 06 07 08 09 10 11

Emergent Technologies & Design 2010-2011

Gabriel Ivorra Morell, Nicolás Villegas Giorgi, Nicolás Leguina de Enterria, Jens Pedersen

EMERGENCE SEMINAR-GROUP 10

general evaluation and conclusions In overall this experiment proved to be quite successful. A deep understanding of the forces and control mechanisms behind, both evolutionary and embryological development was gained. Being able to use scripting as a tool was completely necessary, without it getting rid of the human factor would have been impossible as would have been the possibility of generating enough populations for evolutionary trends to emerge. In general terms looking back in retrospect and with a reductionist approach, it seems that it all came down to being able to control random generative iterative process with simple tools. It constantly seemed that we were trying to balance randomness and organisation, when a too homogeneous population (where all individuals had a high rate of organisation in the direction imposed by the fitness criteria) was reached, a variation generative process was initiated to expand the range of the individuals. It seems logical to think that by the enforcement of direction controlling mechanisms (environmental pressure) and widening mechanisms (mutations, crossbreeding) one could apply this evolutionary trend in a controlled manner to obtain a range of useful end products in any design process.

move x

move y

move z

On a more theoretical level, it seemed that after certain genetic information was stored and passed on from generation to generation, it kept emerging in subsequent generations, even after seeming to disappear when a mutation strategy was applied time and time again it emerged. The conclusion is that the deletion of genetic material seems to be extremely complicated with naturally occurring mechanisms and when enough variation is generated this characteristics will re-appear. Controlling the randomness generative process proved to be a question of selecting the killing strategy carefully, this meant having a clear control over the fitness and being able to select the extreme monsters that where generated when the mutations affected the order of expression of the genetic information in such a way that impossible individuals emerged. Our control over the overall process was quite good. The differentiated fitness criteria proved to be successful in generating body plan specialisation. Although it seems that a more specific criteria would be necessary for all the body parts to have a role in this hierarchical differentiation since many of them did not become fitter, they merely avoided becoming unfit by having less weight on the global scale.

rotate x

rotate y

rotate z

For further investigation it seems that utilising more control mechanisms would be an extremely good approach. One mechanism that we started to develop but didn’t have enough time to follow thoroughly was the control by means of physical constraints. As Sir D’Arcy Wentworth Thompson exposed on his book On Growth and Form, form is not only due evolutionary process but also to the constraints and pressures enforced by the physical world. To include this in our process we started to apply transformations on the reference plane from which the script took its coordinates and which was originally horizontal and at the centroid of the individual. Enclosed are a series of individuals generated by the moving of such plane in the X, Y, and Z directions, respectively, and another set where the same plane is rotated along those same axis. It is clearly visible that such modifications have an immediate effect on the resulting population. It is therefore an extremely powerful tool to control the end product without having to wait for evolution to gradually shift your individuals on the intended direction.

Emergent Technologies & Design 2010-2011

Gabriel Ivorra Morell, Nicolás Villegas Giorgi, Nicolás Leguina de Enterria, Jens Pedersen

general evaluation and conclusions

DEVELOPMENT (RESULT)

SCALE MOVE COPY

= = =

0 1 2

S M C

F(SMC)=S13(0)+M28(1)+C12(2)

8 4

1 UNIFIED GENOME

0.85

UNIFIED GENOME

F(SMC)=S13(0)+M28(1)+C12(2)

1 1

0.69

F(SMC)=S13(0)+M28(1)+C12(2)

4 2

1.76

It is important to understand then that even though all individuals were sorted with the same criteria, every population is inscribed in a specific situation (time) thus what was “the fittest” for one population might not be the fittest again and might not even be obtainable. The idea of a series of genes that slowly became more and more fit over time can only be looked at taking into account the specific environmental conditions and events of individual populations. A genetic algorithm should then understand, adapt and try to control these environmental pressures more than arbitrarily trying to modify a gene pool to obtain a “fit” individual for a given situation. There is no “fittest” when looking at the behaviour of these 11 generations; there are only individuals with specific characteristics under a certain set of pressures, the one with the more suitable characteristics (including chance) gets to pass on its genome to re-create a new population. Taking into account that individuals do not evolve in their lifespan, species, in order to survive most be as diverse as possible. The record keeping system used for the first sequences prove to be an effective way to understand the origins of individuals and it represents an exact set of instructions to replicate this experiment. However, as the complexity of the individuals slowly increased, the task of creating the record became more complicated than the individual itself. It is because of this that the record keeping system for generations 07 trough 11 only shows a formula that represents the incidence of the genome rather than the list of transformations applied to the primitive in order to obtain any given individual.

This new system, is effective in showing the number of specific transformations on a given individual however it does not represent the incidence of this 4 transformations in the fitness criteria. In this sense it needs funder develop2 ment. Gabriel Ivorra Morell, Nicolás Villegas Giorgi, Nicolás Leguina de Enterria, Jens Pedersen 1 8

NOME

3 1 statistical simulation 7

By looking at the point dispersion graph of Generations 01 trough 06 one can see the overall variety of ratios obtain trough “time”, and it is impossible not to ask oneself as for the nature of this change. Why is this occurring from population to population if the set of basic transformation was never changed? CC/CF

TRANSFORMATION

STEP

FROM

UNIFIED GENOME

Emergent Technologies & Design 2010-2011

DEVELOPMENT

QUADRANT

BODY PLAN

normal distribution for 15 individuals

DATA ANALYSIS

INSTRUCTIONS SET (GENOME)

GENERATION

point distribution

INDIVIDUAL

EMERGENCE SEMINAR-GROUP 10

Two concepts that were explored within this experiment were genetic algorithms, and embryological development. A genetic development can be described as a series of instructions acting in a sequential order (a genome) to create a given phenome, the phenome is an expression of the genome which generated it, in other words the phenome becomes a diagram of everything that has affected the genome**. Switching the order of the genome will be of key importance to understand the effect on the phenome and their relation. In order to understand the structure of this process the concepts of Evolutionary Development*** (Evo Devo) were approached. This theory offers an understanding of the closely coupled relation between embryological development and genetic development. Embryological development is a description of the dynamic process of an embryo developing into a differentiated organism, based on a certain genetic input.**** figure 01

figure 02

scientific analogies and conclusions

To attain an understanding of embryologic , and genetic development, the deleauzian concept of an “abstract machine”**** is utilized. To implement such a concept scripting in VB.NET(Visual Basic) is used as the building tool. An “abstract machine” is a “machine” into which different inputs can be fed obtaining an output in return. It is this concept that is abstracted into the correlated relationship between of embryology- and genetic development; see illu 5. The scripting part is then a direct translation of illu 5 into illu 6, so the inputs are the genes (the sequence of instructions), the script becomes “the machine” and the output obtained is the phenotype. Sequence 1: Sequence 1 was a random iterative and additive process whose aim was to create a certain genome, which would generate phenotypes, that according to a given fitness criteria , where fit. The generated gene pool would then be used for further development for the next populations. To learn from the process of sequence 1 August Weismann’s* description of inheritance, was abstracted into what is shown in illustration 1. The abstraction was done because the original description was based on the reproduction cells(sperm/egg) and their independence from the fully grown organism, we on the other hand where more interested in the genes, which are contained within the reproduction cells, not on reproduction itself. The diagram is showing how genes get passed through generations and generate individuals. This diagram was adapted to sequence 1 as seen in illu. 2. This diagram shows how we are using the passing of phenotype and genome through populations, resulting in an accumulation of gene sequences and individuals throughout the first 6 populations. If we where to continue this accumulative asexual reproduction strategy the individuals would continue to get more complicated and homogeneous, which isn’t a desired result. So a shift from illu. 2 to illu. 1 was made; which in turn required a new breeding strategy that was to be applied for the future populations. It was decided that the new breeding strategy would be a translocation mutation of the fittest individual from sequence 1 genome. As illu 1 shows the only thing that would be passed on from this would be the genes. For this new breeding strategy a new script was created. Such “machine” generated individuals based on inputs (genes) and mutations (transformation types and values), this process is described in illu. 3(diagram of pseudo code). To test the genetic algorithm and the use of it, the fitness criteria was changed, this was done to see if we could change the expression of the phenotype from one extreme to another. figure 08

figure 03

Sequence 2: We set out to change the appearance of the individuals based on a new fitness criteria but by using the same genome. It is evident that the populations start to change accordingly, and a more homogeneous population is as the sequence runs. This process is described as “teaching old genes new tricks”, which can be perceived in nature when there is a shift in the environment, which changes the survival criteria for its inhabitants***. Realizing this, shows that it is possible to control a genetic algorithm and steer it in the direction of your desired result. If this process where to continue we would obtain even fitter populations and the populations would get more homogeneous. Population thinkers say that there is no such thing as a “fitter” individual and that you will need a heterogeneous breeding population in order to keep evolution going*****. In order to prevent this homogeneous loop, a cross breeding strategy was chosen to breed “fit” individuals with not “fit” individuals. The breeding strategy is described in illu 9. This crossbreeding strategy was initiated for sequence sequence 3. Sequence 3: The goal for sequence three was to get more diverse populations and, when looking at population 10 it became obvious that by introducing not “fit” individual transformations into the genome more heterogeneous population would be achieved. It was then expected that the same would happen to population 11, but as you can see, the different individuals tend to get really tall and skinny, and this happened without changing the fitness criteria. This was a very unexpected turn of events, this would be an interesting thing to test out, and none the less learn from it, because this is knowledge that will be helpful in the process of controlling a genetic algorithm and its results/output. A thing to keep in mind would be that in order to make these small conclusions more plausible a larger amount of iterations on these two specific topics would be needed. *Brian Goodwin;”how the leopard changed its spots-the evolution of complexity”;Charles Scribner’s Sons;1994;pages 24-31. **D’arcy Wentworth Thompson;”On Growth and Form”;Dover Publications, Inc.;1992 *** Sean B. Carrol; “Endless Forms Most Beautiful-The New Science of Evo Devo”; ****Manuel Delanda; “Deleuze, Diagrams, and the Genesis of Form”; find more specific information about the articles origin... ***** Manuel Delanda;”Deleuze and the use of the genetic algorithm in art”;http://www. youtube.com/watch?v=50-d_J0hKz0; date:20-01-2010 figure 09

figure 04

figure 05

figure 06

Emergent Technologies & Design 2010-2011

Gabriel Ivorra Morell, Nicolás Villegas Giorgi, Nicolás Leguina de Enterria, Jens Pedersen

EMERGENCE SEMINAR-GROUP 10

scripting apPendix

Dim completetree As New Datatree(Of box) Dim boxlist As New list(Of Box) Dim pseudo As New list( Of int32) Dim pl As New list(Of Plane) Call Bodyplan(boxlist, pl) Dim pl1 As plane = pl.item(0) Dim transout As New Datatree(Of Integer) Dim scaleout As New Datatree( Of Integer) Dim moveout As New Datatree(Of Double) Dim copyout As New Datatree(Of Double) ‘----------------------------------------------------------------------‘if we are dividing into lower and upper bodyparts the code should be ‘for v as int32 = 0 to boxlist.count-1. ‘if we are doing sexual reproductiong the code need to run twice, one for each ‘bodypart-superior/inferior ‘-----------------------------------------------------------------------For v As int32 = 0 To 3 ‘seperation in bodyplan(inferior/superior)/if this is activated then we ‘seperate into superior And inferior body parts ‘---------------------------------------------------------------------‘ If v >= (boxlist.count - 1) / 2 Then trans_number = trans_number_2 ‘ If v >= (boxlist.count - 1) / 2 Then copy_trans = copy_trans_2 ‘ If v >= (boxlist.count - 1) / 2 Then scale_trans = scale_trans_2 ‘ If v >= (boxlist.count - 1) / 2 Then move_trans = move_trans_2 ‘----------------------------------------------------------------------

Dim translist2 As New list(Of int32) For t As int32 = 0 To n_trans - 1 Dim rndval As Integer = math.round((trans_ number_2.Count - 1) * rnd) Dim trans As Integer = trans_number_2. item(rndval) translist2.add(trans) Next

item(v)

‘Defining transformation Dim p3 As New GH_Path(v) Dim translist As New list(Of int32) Dim n_trans As Integer = n_transformations.

For t As int32 = 0 To n_trans - 1 Dim rndval As Integer = math.round((trans_ number.Count - 1) * rnd) Dim trans As Integer = trans_number. item(rndval) translist.add(trans) Next

Emergent Technologies & Design 2010-2011

‘Sexual reproduction ‘----------------------------------------------------‘ ‘sexual reproduction part half/half ‘ For h As int32 = 1 To n_trans ‘ If h > n_trans / 2 Then ‘ pseudo.add(translist.item(h)) ‘ Else ‘ pseudo.add(translist2.item(h)) ‘ End If ‘ Next ‘sexual reproduction part weave For h As int32 = 0 To n_trans - 1 If h Mod 2 = 0 Then pseudo.add(translist. item(h)) If h Mod 2 = 1 Then pseudo.add(translist2. item(h)) Next ‘----------------------------------------------------‘defining trasnformation trees/arrays ‘----------------------------------------------------translist = pseudo transout.addrange(translist, p3) Dim new_bx_l As New List(Of Box) Dim tree_move As New Datatree(Of Double) Dim tree_copy As New Datatree(Of Double) Dim tree_scale As New Datatree(Of Integer) Dim tree_move_2 As New Datatree(Of Double) Dim tree_copy_2 As New Datatree(Of Double) Dim tree_scale_2 As New Datatree(Of Integer) Dim f As Int32 = 0 For i As int32 = 0 To scale_trans.Count - 1 Dim ph As New GH_path(f) tree_scale.add(scale_trans.item(i), ph) tree_scale.add(scale_trans.item(i + 1), ph) tree_scale.add(scale_trans.item(i + 2), ph) i = i + 2 f += 1 Next f = 0 For j As int32 = 0 To move_trans.Count - 1

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Dim ph As New GH_path(f) tree_move.add(move_trans.item(j), ph) tree_move.add(move_trans.item(j + 1), ph) tree_move.add(move_trans.item(j + 2), ph) j = j + 2 f = f + 1 Next f = 0 For k As int32 = 0 To copy_trans.Count - 1 Dim ph As New GH_path(f) tree_copy.add(copy_trans(k), ph) tree_copy.add(copy_trans(k + 1), ph) tree_copy.add(copy_trans(k + 2), ph) k = k + 2 f = f + 1 Next f = 0 For o As int32 = 0 To scale_trans_2.Count Dim ph As New GH_path(f) tree_scale_2.add(scale_trans_2.item(o), ph) tree_scale_2.add(scale_trans_2.item(o +

1), ph) 2), ph)

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o = o + 2 f += 1 Next f = 0 For u As int32 = 0 To move_trans_2.Count Dim ph As New GH_path(f) tree_move_2.add(move_trans_2.item(u), ph) tree_move_2.add(move_trans_2.item(u +

1), ph) 2), ph)

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ph) ph)

tree_scale_2.add(scale_trans_2.item(o +

tree_move_2.add(move_trans_2.item(u +

u = u + 2 f = f + 1 Next f = 0 For g As int32 = 0 To copy_trans_2.Count Dim ph As New GH_path(f) tree_copy_2.add(copy_trans_2.item(g), ph) tree_copy_2.add(copy_trans_2.item(g + 1), tree_copy_2.add(copy_trans_2.item(g + 2),

g = g + 2 f = f + 1 Next ‘-----------------------------------------------------------

Gabriel Ivorra Morell, Nicolás Villegas Giorgi, Nicolás Leguina de Enterria, Jens Pedersen

EMERGENCE SEMINAR-GROUP 10 ‘doing the transformations ‘----------------------------------------------------------new_bx_l.add(boxlist.item(v)) Dim int As Int32 = 0 Dim x As vector3d = pl1.XAxis Dim y As vector3d = pl1.YAxis Dim z As vector3d = pl1.ZAxis For q As int32 = 0 To n_trans - 1 ‘Which trnsformation Dim trans As Integer = translist.item(q) print(trans) Dim rndnummo,rndnumco,rndnumsc As int32 If q Mod 2 = 0 Then rndnummo = math.round((tree_move. branchcount - 1) * rnd) rndnumco = math.round((tree_copy. branchcount - 1) * rnd) rndnumsc = math.round((tree_scale. branchcount - 1) * rnd) Else rndnummo = math.round((tree_move_2. branchcount - 1) * rnd) rndnumco = math.round((tree_copy_2. branchcount - 1) * rnd) rndnumsc = math.round((tree_scale_2. branchcount - 1) * rnd) tree_scale = tree_scale_2 tree_copy = tree_copy_2 tree_move = tree_move_2 End If For p As int32 = 0 To int ‘Doing the transformation based on the chosen transformation number If trans = 1 Then Call move(p, rndnummo, new_bx_l, tree_move, x, y, z) If trans = 2 Then Call copy(pl1, p, rndnumco, new_bx_l, tree_copy, x, y, z) If trans = 0 Then Call scale(p, rndnumsc, new_bx_l, tree_scale, pl1) Next ‘-------------------------------------------------------------------‘adding values to seperate lists: ‘---------------------------------------------------------------------If trans = 0 Then Dim tempnmblist As New List (Of Integer) tempnmblist.Add(tree_scale. branch(rndnumsc).item(0))

Emergent Technologies & Design 2010-2011

scripting apPendix tempnmblist.Add(tree_scale. branch(rndnumsc).item(1)) tempnmblist.Add(tree_scale. branch(rndnumsc).item(2)) scaleout.addrange(tempnmblist, p3) End If If trans = 1 Then Dim tempnmblist As New List (Of Double) tempnmblist.Add(tree_move. branch(rndnummo).item(0)) tempnmblist.Add(tree_move. branch(rndnummo).item(1)) tempnmblist.Add(tree_move. branch(rndnummo).item(2)) moveout.addrange(tempnmblist, p3) End If If trans = 2 Then Dim tempnmblist As New List (Of Double) tempnmblist.Add(tree_copy. branch(rndnumco).item(0)) tempnmblist.Add(tree_copy. branch(rndnumco).item(1)) tempnmblist.Add(tree_copy. branch(rndnumco).item(2)) copyout.addrange(tempnmblist, p3) End If ‘----------------------------------------------------------------------------‘redifining transformation plane ‘-----------------------------------------------------------------------------Dim temp As New list(Of point3d) Dim lx As New list (Of Double) Dim ly As New list (Of Double) Dim lz As New list (Of Double) For p As int32 = 0 To new_bx_l.count - 1 Dim bx As Box = new_bx_l.item(p) Dim intx As Double = bx.Center(0) Dim inty As Double = bx.Center(1) Dim intz As Double = bx.Center(2) lx.add(intx) ly.add(inty) lz.add(intz) Next Dim sx As Double = lx.item(0) Dim sy As Double = ly.item(0) Dim sz As Double = lz.item(0) For e As int32 = 1 To new_bx_l.count - 1 Dim s1x As Double = sx + lx.item(e) Dim s1y As Double = sy + ly.item(e)

Dim s1z As Double = sz + lz.item(e) sx = s1x sy = s1y sz = s1z Next Dim test_pt As New point3d((sx / new_ bx_l.count), ( sy / new_bx_l.count), (sz / new_ bx_l.count)) Dim pltest As New plane(test_pt, plane. WorldXY.ZAxis) pl1 = pltest ‘-----------------------------------------------------------------------------‘adding transformed objects to a list int = new_bx_l.Count - 1 Next ‘making the transformed output Print(“bodypart “ & v) For d As int32 = 0 To new_bx_l.Count - 1 Dim p2 As New GH_Path(v) completetree.add(new_bx_l.item(d), p2) Next Next ‘getting stuff out of the code a = completetree tr = transout sc = scaleout co = copyout mo = moveout

End Sub ‘<Custom additional code> Sub move (p As Integer, ByVal rndnummo As Integer, ByVal new_bx_l As List(Of box), ByVal tree_ move As datatree(Of Double), ByVal x As Vector3d, ByVal y As Vector3d, ByVal z As Vector3d) Dim bx1 As box = new_bx_l.item(p) Dim a As New Vector3d(x.X * tree_move. branch(rndnummo).item(0), x.y * tree_move. branch(rndnummo).item(0), x.z * tree_move. branch(rndnummo).item(0)) Dim b As New Vector3d(y.X * tree_move. branch(rndnummo).item(1), y.y * tree_move. branch(rndnummo).item(1), y.z * tree_move.

Gabriel Ivorra Morell, Nicolás Villegas Giorgi, Nicolás Leguina de Enterria, Jens Pedersen

EMERGENCE SEMINAR-GROUP 10 branch(rndnummo).item(1)) Dim c As New Vector3d(z.X * tree_move. branch(rndnummo).item(2), z.y * tree_move. branch(rndnummo).item(2), z.z * tree_move. branch(rndnummo).item(2)) Dim mvx As transform = transform. Translation(a) Dim mvy As transform = transform.Translation(b) Dim mvz As transform = transform.Translation(c) bx1.transform(mvx) bx1.transform(mvy) bx1.transform(mvz) new_bx_l.item(p) = bx1 print(“move: “ & “ X: “ & tree_move. branch(rndnummo).item(0) & “ Y: “ & tree_move. branch(rndnummo).item(1) & “ Z: “ & tree_move. branch(rndnummo).item(2)) End Sub Sub copy(ByVal pl1 As Plane, ByVal p As Integer, ByVal rndnumco As Integer, ByVal new_bx_l As List(Of box), ByVal tree_copy As datatree(Of Double), ByVal x As Vector3d, ByVal y As Vector3d, ByVal z As Vector3d) Dim bx1 As box = new_bx_l.item(p) Dim br As Brep = bx1.ToBrep Dim bx2 As New box(pl1, br.DuplicateVertices) Dim a As New Vector3d(x.X * tree_copy. branch(rndnumco).item(0), x.y * tree_copy. branch(rndnumco).item(0), x.z * tree_copy. branch(rndnumco).item(0)) Dim b As New Vector3d(y.X * tree_copy. branch(rndnumco).item(1), y.y * tree_copy. branch(rndnumco).item(1), y.z * tree_copy. branch(rndnumco).item(1)) Dim c As New Vector3d(z.X * tree_copy. branch(rndnumco).item(2), z.y * tree_copy. branch(rndnumco).item(2), z.z * tree_copy. branch(rndnumco).item(2)) Dim mvx As transform = transform. Translation(a) Dim mvy As transform = transform.Translation(b) Dim mvz As transform = transform.Translation(c) bx2.transform(mvx) bx2.transform(mvy) bx2.transform(mvz) new_bx_l.add(bx2) print(“copy:” & “ X: “ & tree_copy. branch(rndnumco).item(0) & “ Y: “ & tree_copy. branch(rndnumco).item(1) & “ Z: “ & tree_copy. branch(rndnumco).item(2))

Emergent Technologies & Design 2010-2011

scripting apPendix End Sub Sub scale(ByVal p As Integer, ByVal rndnumsc As Integer, ByVal new_bx_l As List(Of box), ByVal tree_ scale As datatree(Of Integer), ByVal pl As plane) Dim bx1 As box = new_bx_l.item(p) Dim a As Integer = tree_scale. branch(rndnumsc).item(0) Dim b As Integer = tree_scale. branch(rndnumsc).item(1) Dim c As Integer = tree_scale. branch(rndnumsc).item(2) Dim esc As transform = Transform.Scale(pl, a, b, c) bx1.transform(esc) new_bx_l.item(p) = bx1 print(“scale: “ & “ factorX: “ & tree_scale. branch(rndnumsc).item(0) & “ factorY: “ & tree_ scale.branch(rndnumsc).item(1) & “ factorZ: “ & tree_scale.branch(rndnumsc).item(2)) End Sub Sub bodyplan(ByVal boxlist As list(Of box), ByVal pl As list(Of Plane)) Dim x As New Interval(0, 10) Dim y As New Interval(0, 10) Dim z As New Interval(0, 10) Dim i_box As New Box(plane.WorldXY, x, y, z) boxlist.add(i_box) Dim pt() As point3d = i_box.GetCorners Dim stpt As point3d = pt(0) Dim ept1 As Point3d = pt(1) Dim ept2 As point3d = pt(3) Dim ept3 As Point3d = pt(4) Dim vec1 As New Vector3d(ept1.X - stpt.X, ept1.y - stpt.y, ept1.z - stpt.z) Dim vec2 As New Vector3d(ept2.X - stpt.X, ept2.y - stpt.y, ept2.z - stpt.z) Dim vec3 As New Vector3d(ept3.X - stpt.X, ept3.y - stpt.y, ept3.z - stpt.z) Dim vec4 As New vector3d(vec1.X + vec2.X, vec1.y + vec2.y, vec1.z + vec2.z) Dim tr1 As transform = transform. Translation(vec2) Dim tr2 As transform = transform. Translation(vec1) Dim tr3 As transform = transform.Translation(vec2) i_box.Transform(tr1) boxlist.add(i_box) i_box.Transform(tr2) boxlist.add(i_box)

i_box.Transform(tr3) boxlist.add(i_box) For i As int32 = 0 To boxlist.count - 1 Dim tr As transform = transform. Translation(vec3) Dim bx As Box = boxlist.item(i) bx.Transform(tr) boxlist.add(bx) Next Dim temp As New list(Of point3d) Dim lx As New list (Of Double) Dim ly As New list (Of Double) Dim lz As New list (Of Double) For q As int32 = 0 To boxlist.count - 1 Dim bx As Box = boxlist.item(q) Dim intx As Double = bx.Center(0) Dim inty As Double = bx.Center(1) Dim intz As Double = bx.Center(2) lx.add(intx) ly.add(inty) lz.add(intz) Next Dim sx As Double = lx.item(0) Dim sy As Double = ly.item(0) Dim sz As Double = lz.item(0) For e As int32 = 1 To boxlist.count - 1 Dim s1x As Double = sx + lx.item(e) Dim s1y As Double = sy + ly.item(e) Dim s1z As Double = sz + lz.item(e) sx = s1x sy = s1y sz = s1z Next Dim test_pt As New point3d((sx / boxlist. count), ( sy / boxlist.count), (sz / boxlist.count)) Dim pltest As New plane(test_pt, plane.WorldXY. ZAxis) pl.add(pltest) End Sub

Gabriel Ivorra Morell, Nicolás Villegas Giorgi, Nicolás Leguina de Enterria, Jens Pedersen