EMergent TEChnologies and design | Core 01

EMERGENCE SEMINAR DOCUMENTATION Emergent Technologies and Design 2012

Ulises Juliao Georgios Maragkos Michela Musto Miguel Rus

PROGRAMME:

AA Emergent Technologies and Design

TERM:

02

STUDENT NAMES:

Ulises Juliao, Georgios Maragkos, Michela Musto, Miguel Rus

SUBMISSION TITLE:

Emergence Seminar - Documentation

COURSE TITLE:

Emergence Seminar

DECLARATION:

We certify that this piece of work is entirely our own and that any quotation or paraphrase from the published or unpublished work or other is duly acknowledged

DATE:

11/02/ 2013

SIGNATURES:

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Contents Abstract................................................................05 Sequence_1 Body plan..............................................................06 Population 01........................................................08 Population 02........................................................10 Population 03........................................................12 Population 04........................................................14 Population 05........................................................19 Conclusion............................................................20 Sequence_2 Body Plan..............................................................24 Population 06........................................................26 Population 07........................................................28 Population 07A......................................................30 Population 08........................................................32 Conclusion............................................................37 Sequence_3 Body Plan..............................................................40 Population 09........................................................42 Population 10........................................................44 Population 11........................................................46 Population 11A......................................................48 Conclusion............................................................52 Computational Model Population 9..........................................................06 Population 10........................................................06 Population 11........................................................06 Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

ABSTRACT This paper is an attempt to examine and apply the evolutionary techniques and processes of genetic algorithms in computation. The process of growth in the living organisms has always been a field of study in the scientific world and more particularly in evolutionary computation. The applications of these biological processes of growth and evolution through genetic algorithms will be examined in this emergence seminar, through a series of evolutionary techniques in design.

EVO DEVO The variation and multiplicity on the processes of growth and development of living organisms through the decoding of the DNA, was a key aspect in which evolutionary computation started to evolve. The simulation of biological procedures of development is the main theme in evolutionary computation. According to Evo Devo: every animal form is the product of two processes – development from an egg and evolution from its ancestors. In our experiments, we will consider as ancestors a primitive geometrical form from which through evolution, we will produce populations by the criteria of the most adapted creatures. The form of every individual in our population is the result of a set of combined informations the “genes” . The combined genes become the ‘’genome’’ and express the formal characteristic of its individual. In the living organisms the DNA contains “ genomes ‘’ that expresses the characteristics of the living persons. The frequency and the process in which each gene appears in each individual is major issue that evolutionary computation examines.

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

EMERGENCE SEMINAR Sequence 01

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Sequence 01 Body Plan Body plan structure: Instructions Scale Scale, ScalingFactor, Point, Copy eg. S[1.2,CM,T] Scale 1D Scale, Vector, ScalingFactor, Point, Copy eg. S1[Y,1.2,CM,F] Scale 2D Scale, Plane, ScalingFactor, Point, Copy eg. S2[XY,1.2,CM,F] Mirror Mirror, Plane, Point, Copy (copy true or false) eg. MI[YZ,BN,T] Move Move, Vector eg. M[(7.5,0,0)] Copy Copy, Vector eg. C[(1,0,0)] Rotate Rotate, Plane, Point, Degree eg. R[XY, BE,45] Polar Array PolarArray, Plane, Point, Degree, NumberOfCopies eg. PA[YZ, BW, 15, 5]

The body plan defines details of the individual structure on its body part. Powerful changes in the organizing of genes have tremendous repercussion in the final shape, size and number of parts in every living creature. The emergence phenomena of having small complex changes that arise in even larger more complex system and organizations is built over time by alterations to the existing forms.

T

Points

CM, BN, BE, BS, BW, T, CM’

BN

BW CM CM’

Fitness Criteria

BE

Surface Area/ Volume BS

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

BREEDING STRATEGIES Parent 1

Parent 2

Sequence 01

Parent 1

Parent 2

Parent 1

Parent Breeding 2 Parent 1 Parent 2

Strategy

P

Parents creation: The development of the first generation (theSTRATEGIES procreBREEDING ative) its based in a random selection of genes(rhino commands) and their agroupation in genomes (group of genes). The amount of genes Parent varies from two (2) 2to Parent 1 1 Parent seven (7) in order to test in the evolution process how the genomes perform and evaluate if the amount of genes inside each of them (genomes), have a crucial repercution for the next generation.

G STRATEGIES Parent 2

endant

Descendant Parent 1

Parent 2

Parent 1

Parent 2

Descendant Parent 2

Descendant Parent 1 Parent 2

Parent 1

Parent 2

Descendant Parent 1 Parent 2

Descendant Parent 1 Parent 2

Descendant Parent 1 Parent 2

Descendant

Descendant

Parent 1 Parent 2

Parent 1 Parent 2

Descendant

Descendant Parent 1 Parent 2

MUTATIONS Insertion Deletion

Duplicatio Descendant

Descendant

Sequence_01

Descendant

Descendant

Inversion

Descendant

Killing Strategy

& 10 Unfit Individuals 2/3 Killed

Sequence_02

Descendant

Split Geometries

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Sequence 01 Population 01

GEN 01.01 S[1.5,CM,F] R[ZX,CM,30] MI[ZY,CM,T] S1[X,1.2,CM,F] M[(0.5,0,0)]

GEN 01.06 C[0.65,0,0] R[XY,CM,45] R[ZX,CM,-45] MI[ZX,CM,T] MI[ZX,CM,T] C[-1.30,0,0] R[ZX,CM,45]

GEN 01.11 C[(2,0,0)] PA[XY,CM,30,3] S2[XY,2,CM,F]

GEN 01.02

S2[XY,1.2,CM,F] S[10,CM,F] C[(0,0,-1)] S[0.5,CM,F] R[ZY,CM,45]

GEN 01.03 C[(0.5,0,0)] R[XY,CM,45] S1[Z,2.0,CM,F]

GEN 01.07

GEN 01.08

GEN 01.12

GEN 01.13

S1[X,3.0,CM (0.707,0,-0.175,F] PA[YX,CM (0.8,0,0),180, 5]

C[(1,0,0)] S2[XY,2,CM,F] PA[XY,CM,30,2]

C[(0,5,0,0)] R[XY,CM,45] PA[ZX,CM,40·2]

S1[Y,3,CM,F] C[(0.43,0,0)] R[ZY,BN,30]

GEN 01.04

R[ZY, CM,45] MI[ZX,CM,T] S1[Y,1.5,CM,F] PA[XY,BS,45,2] PA[XY,BS,-90,2]

GEN 01.09

S2[XY,1.5,CM,F] PA[XY,CM,30,3]

GEN 01.14 S2[XY,2,CM,F] C[(0.42,0,0)] C[(0.83,0,0)] 2

GEN 01.05 MI[ZY,CM’,T] R[XY,CM,45] M[(0,0,0.25)] S[3.0,CM,F]

GEN 01.10 R[XY,CM,30] S1[Y,1.5,CM,F]

GEN 01.15

C[(1,8,0,0)] S2[XY,1.5,CM,F] R[ZX, CM,30]

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Individual

Surface Area

Volume

SA/V

Individuals Ranked

SA/V (Ordered)

G1- 01

7.43

0.92

8.08

G1-13

14.06

G1- 02

18.03

4.09

4.41

G1-06

10.14

G1- 03

5.38

0.61

8.82

G1-11

10.62

G1- 04

8.68

1

8.68

G1-07

9.72

G1- 05

34.13

9.29

3.67

G1-12

9.56

G1- 06

9.45

0.88

10.74

G1-14

9.43

G1- 07

19.54

2.01

9.72

G1-10

8.86

G1- 08

12.29

1.59

7.73

G1-03

8.82

G1- 09

32.78

5.65

5.80

G1-04

8.68

G1-10

11.43

1.29

8.86

G1-15

8.61

G1- 11

8.07

0.76

10.62

G1-01

8.08

G1- 12

14.34

1.5

9.56

G1-08

7.73

G1- 13

12.23

0.87

14.06

G1-09

5.80

G1- 14

36.41

3.86

9.42

G1-05

3.67

G1- 15

8.09

0.94

8.61

G1-02

3.00

Sequence 01 Population 01

Mean Standard Deviation

8.49 2.66

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Sequence 01

ASEXUAL REPRODUCTION

GEN 02.01 S[1.5,CM,F] R[ZX,CM,30] MI[ZY,CM,T] S1[X,1.2,CM,F] M[(0.5,0,0)]

GEN 02.02

S2[XY,1.2,CM,F] S[10,CM,F] C[(0,0,-1)] S[0.5,CM,F] R[ZY,CM,45]

GEN 02.03 C[(0.5,0,0)] R[XY,CM,45] S1[Z,2.0,CM,F]

Population 02

GEN 02.04

GEN 02.05

R[ZY, CM,45] MI[ZX,CM,T] S1[Y,1.5,CM,F] PA[XY,BS,45,2] PA[XY,BS,-90,2]

MI[ZY,CM’,T] R[XY,CM,45] M[(0,0,0.25)] S[3.0,CM,F]

Modified

GEN 02.06 C[0.65,0,0] R[XY,CM,45] R[ZX,CM,-45] MI[ZX,CM,T] MI[ZX,CM,T] C[-1.30,0,0] R[ZX,CM,45]

GEN 02.07

S1[X,3.0,CM (0.707,0,-0.175,F] PA[YX,CM (0.8,0,0),180, 5]

GEN 02.08 C[(0,5,0,0)] R[XY,CM,45] PA[ZX,CM,40 2]

GEN 02.10

GEN 02.09

R[XY,CM,30] S1[Y,2.5,CM,F]

S2[XY,1.5,CM,F] PA[XY,CM,30,3]

Modified

GEN 02.11 C[(2,0,0)] PA[XY,CM,30,3] S2[XY,2,CM,F]

GEN 02.12 C[(1,0,0)] S2[XY,2,CM,F] PA[XY,CM,30,2]

GEN 02.13 S1[Y,3,CM,F] C[(0.43,0,0)] R[ZY,BN,30]

GEN 02.14

S2[XY,3.5,CM,F] C[(0.42,0,0)] C[(0.83,0,0)] 2

GEN 02.15

C[(1,8,0,0)] S2[XY,1.5,CM,F] R[ZX, CM,30]

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Individual

Surface Area

Volume

SA/V

Individuals Ranked

SA/V (Ordered)

G2- 01

7.43

0.92

8.08

G2-06

10.74

G2- 02

18.03

4.09

4.41

G2-11

10.62

G2- 03

5.38

0.61

8.82

G2-07

9.72

G2- 04

8.68

1

8.68

G2-12

9.56

G2- 05

34.13

9.29

3.67

G2-03

8.82

G2- 06

9.45

0.88

10.74

G2-04

8.68

G2- 07

19.54

2.01

9.72

G2-15

8.61

G2- 08

12.29

1.59

7.73

G2-01

8.08

G2- 09

32.78

5.65

5.80

G2-08

7.73

G2-10

9.52

1.29

8.86

G2-14

7.69

G2- 11

8.07

0.76

10.62

G2-13

7.60

G2- 12

14.34

1.5

9.56

G2-10

7.38

G2- 13

12.23

0.87

14.06

G2-09

5.80

G2- 14

30.00

3.90

7.69

G2-02

4.41

G2- 15

8.09

0.94

8.61

G2-05

3.67

Sequence 01 Population 02 A more balanced population: In order to start a clear understading of what the cross-breeding will come up, two(2) individuals (10 and 14) were modified with the intention of have a better distribution of individuals inside the population. So the entire generation has an even distribution through the standard deviation diagram.

Mean Standard Deviation

7.94 1.97

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Sequence 01 Population 03

GEN 03.01 C[0.65,0,0] R[XY,CM,45] R[ZX,CM,-45] MI[ZX,CM,T] C[(2,0,0)] PA[XY,CM,30,3]

GEN 03.06 R[XY,CM,45] R[ZX,CM,-45] MI[ZX,CM,T] MI[ZX,CM,T] S2[XY,2,CM,F]

GEN 03.11

S1[X,3.0 CM(0.707,0,-0.175,F] PA[YX,CM(0.8,0,0),180, 5] C[(2,0,0)] PA[XY,CM,30,3] S2[XY,2,CM,F]

GEN 03.02

S1[X,3.0 CM(0.707,0,-0.175,F] PA[YX CM(0.8,0,0),180, 5][(1,0,0)] S2[XY,2,CM,F] PA[XY,CM,30,2]

GEN 03.03

S1[X,3.0 CM(0.707,0,-0.175,F] PA[YX,CM(0.8,0,0),180, 5] S2[XY,2,CM,F] C[(0.42,0,0)] C[(0.83,0,0)] 2

GEN 03.07

GEN 03.08

R[XY,CM,45] R[ZX,CM,-45] MI[ZX,CM,T] MI[ZX,CM,T] S2[XY,2,CM,F]

R[ZX,CM,-45] MI[ZX,CM,T] MI[ZX,CM,T] S2[XY,2,CM,F]

GEN 03.12

GEN 03.13

C[0.65,0,0] R[XY,CM,45] R[ZX,CM,-45] C[(1,0,0)] S2[XY,2,CM,F]

PA[XY,CM,30,3] S2[XY,2,CM,F] C[(0.42,0,0)] C[(0.83,0,0)] 2

GEN 03.04

S1[X,3.0 CM(0.707,0,-0.175,F] PA[YX,CM(0.8,0,0),180, 5] C[(2,0,0)] PA[XY,CM,30,3] S2[XY,2,CM,F]

GEN 03.09 C[(2,0,0)] PA[XY,CM,30,3] S2[XY,2,CM,F] C[(1,0,0)] S2[XY,2,CM,F] PA[XY,CM,30,2]

GEN 03.14 S2[XY,2,CM,F] S2[XY,2,CM,F] C[(0.42,0,0)] C[(0.83,0,0)] 2

GEN 03.05

S1[X,3.0 CM(0.707,0,-0.175,F] PA[YX, CM(0.8,0,0),180, 5] S2[XY,2,CM,F]

GEN 03.10 C[0.65,0,0] R[XY,CM,45] R[ZX,CM,-45] MI[ZX,CM,T] C[(2,0,0)] S2[XY,2,CM,F] C[(0.42,0,0)]

GEN 03.15 C[(1,0,0)] PA[XY,CM,30,2] S2[XY,2,CM,F] C[(0.42,0,0)]

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Individual

Surface Area

Volume

SA/V

Individuals Ranked

SA/V (Ordered)

G3- 01

8.89

0.80

11.1

G3-01

11.11

G3- 02

22.47

2.35

9.56

G3-11

9.67

G3- 03

43.60

5.21

8.37

G3-02

9.56

G3- 04

38.39

4.45

8.63

G3-07

9.53

G3- 05

32.17

3.74

8.60

G3-12

9.35

G3- 06

10.80

1.34

8.06

G3-04

8.63

G3- 07

25.16

2.64

9.53

G3-15

8.62

G3- 08

7.58

0.97

7.81

G3-05

8.60

G3- 09

15.64

1.87

8.36

G3-03

8.37

G3-10

13.17

1.92

6.86

G3-09

8.36

G3- 11

27.27

2.82

9.67

G3-13

8.15

G3- 12

12.06

1.29

9.35

G3-06

8.06

G3- 13

15.33

1.88

8.15

G3-14

7.82

G3- 14

46.43

5.94

7.82

G3-08

7.81

G3- 15

12.50

1.45

8.62

G3-10

6.86

Sequence 01 Population 03 Addition of Cross-Breeding: By the selection of the five (5) fittest in the second generation (G02) and crossing them amongst each other was the first decision of the evolutionary development in order to start improving the mean population to get fittest individuals.

Mean Standard Deviation

8.70 0.98

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Sequence 01 Population 04

GEN 04.01

GEN 04.02

C[0.65,0,0] R[XY,CM,45] PA[XY,CM,30,3] MI[ZX,CM,T] S2[XY,2,CM,F]

S1[X,3.0,CM(0.707,0,-0.175,F] PA[YX,CM(0.8,0,0),180, 5] MI[ZX,CM,T] C[0.40,0,0] PA[XY,CM,30,2]

GEN 04.03

GEN 04.04

C[0.65,0,0] R[XY,CM,45] C[(1,0,0)] S2[XY,2,CM,F] C[(2,0,0)] PA[XY,CM,30,3]

C[0.65,0,0] R[XY,CM,45] R[ZX,CM,-45] MI[ZX,CM,T] MI[ZX,CM,T] S2[XY,2,CM,F]

GEN 04.05

S1[X,3.0,CM(0.707,0,-0.175,F] PA[YX,CM(0.8,0,0),180, 5] C[(1,0,0)] MI[ZX,CM,T] C[0.40,0,0] S2[XY,2,CM,F]

Mutated

GEN 04.06 R[XY,CM,45] C[0.65,0,0] MI[ZX,CM,T] MI[ZX,CM,T] C[-1.30,0,0] S2[XY,2,CM,F]

GEN 04.07

GEN 04.08

M[0.40,0,0] R[XY,CM,45] PA[XY,CM,30,3] MI[ZX,CM,T] MI[ZX,CM,T] S2[XY,2,CM,F]

R[XY,CM,45] C[0.65,0,0] MI[ZX,CM,T] C[0.40,0,0] S2[XY,2,CM,F]

GEN 04.12

GEN 04.13

GEN 04.09

R[XY,CM,45] C[0.65,0,0] C[(2,0,0)] PA[XY,CM,30,3] S2[XY,2,CM,F]

GEN 04.10 C[0.65,0,0] R[XY,CM,45] C[(2,0,0)] PA[XY,CM,30,3] S2[XY,2,CM,F]

Mutated

GEN 04.11 R[XY,CM,45] C[0.65,0,0] R[ZX,CM,-45] C[(1,0,0)] S2[XY,2,CM,F]

S2[XY,2,CM,F] PA[YX,CM(.8,0,0),180, 5]0 R[ZX,CM,-45] C[(1,0,0)] S2[XY,2,CM,F]

C[0.65,0,0] R[XY,CM,45] PA[XY,CM,30,3] MI[ZX,CM,T] S2[XY,2,CM,F]

GEN 04.14 C[0.65,0,0] R[XY,CM,45] C[(2,0,0)] PA[XY,CM,30,3] S2[XY,2,CM,F]

GEN 04.15

S2[XY,2,CM,F] PA[YX,CM(.8,0,0),180, 5] R[ZX,CM,-45] C[(1,0,0)] S2[XY,2,CM,F]

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Individual

Surface Area

Volume

SA/V

Individuals Ranked

SA/V (Ordered)

G4- 01

12.95

1.36

9.52

G4- 02

10.36

G4- 02

32.00

3.09

10.36

G4- 10

9.83

G4- 03

21.58

2.36

9.14

G4- 07

9.78

G4- 04

10.61

1.14

9.31

G4- 09

9.72

G4- 05

65.45

7.46

8.77

G4- 01

9.52

G4- 06

12.20

1.32

9.24

G4- 13

9.36

G4- 07

11.73

1.20

9.78

G4- 04

9.31

G4- 08

33.06

3.78

8.75

G4- 14

9.26

G4- 09

15.16

1.56

9.72

G4- 06

9.24

G4-10

15.53

1.59

9.83

G4- 03

9.14

G4- 11

16.26

1.88

8.65

G4- 05

8.77

G4- 12

81.01

10.95

7.40

G4- 08

8.75

G4- 13

13.11

1.40

9.36

G4- 11

8.65

G4- 14

12.68

1.37

9.26

G4- 15

8.53

G4- 15

38.88

4.56

8.53

G4- 12

7.40

Sequence 01 Population 04 The five fittest: The selection of the five fittest of the generation 03 (G03) was the selection criteria to create this new generation 04 (G04). This process reflected a clear improvement in the whole generation by the increase on the mean.

Adding mutation: Three individuals (8,11,13) which are killed by the split geometry criteria in the killing strategies were intended to be save by the addition of mutations to all of them(insertion, duplication, inversion) respectively. The result was that one of them was saved and became one of the three(3) fittest to create the generation 05(G05).

Fit to Fit: To complete the other two(2) individuals in order to have the five(5) individuals to make the crossing process, a new strategy was applied.

The two fittest of Generation03(G03) were paired to create the last two(2) fittest individuals that we called “Fittest A” and “Fittest B”.

Mean Standard Deviation

9.17 0.68

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Sequence 01 Mutations

DUPLICATION GEN 04.08

GEN 04.08 R[XY,CM,45] C[0.65,0,0] MI[ZX,CM,T] C[0.40,0,0] S2[XY,2,CM,F]

R

C

MI C

S2

R

Original Genome

C

MI C

S2 S2

Mutated Genome

R[XY,CM,45] C[0.65,0,0] MI[ZX,CM,T] C[0.40,0,0] S2[XY,2,CM,F] S2[XY,2,CM,F]

Mutation Typologies

Insertion INSERTION GEN 04.11 R[XY,CM,45] C[0.65,0,0] R[ZX,CM,-45] C[(1,0,0)] S2[XY,2,CM,F]

GEN 04.11

R

C

R

C

S2

R

C

R

C

S2 PA

Mutated Genome

Original Genome

R[XY,CM,45] C[0.65,0,0] R[ZX,CM,-45] C[(1,0,0)] S2[XY,2,CM,F] PA[XY,CM,30,3]

Deletion Duplication Inversion

DUPLICATION GEN 04.13 C[0.65,0,0] R[XY,CM,45] PA[XY,CM,30,3] MI[ZX,CM,T] S2[XY,2,CM,F]

GEN 04.13

C

R

PA MI S2

Original Genome

C

R

MI PA S2

Mutated Genome

C[0.65,0,0] R[XY,CM,45] MI[ZX,CM,T] PA[XY,CM,30,3] S2[XY,2,CM,F]

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Sequence 01 Population 05

GEN 05.01

GEN 05.02

GEN 05.03

GEN 05.06

GEN 05.07

GEN 05.08

C[0.65,0,0] R[XY,CM,45] PA[XY,CM,30,3] MI[ZX,CM,T] S2[XY,2,CM,F]

C[0.65,0,0] R[XY,CM,45] PA[XY,CM,30,3] MI[ZX,CM,T] S2[XY,2,CM,F]

GEN 05.11

GEN 05.12

GEN 05.13

C[0.65,0,0] R[XY,CM,45] PA[XY,CM,30,3] MI[ZX,CM,T] MI[ZX,CM,T] S2[XY,2,CM,F]

C[0.65,0,0] R[XY,CM,45] PA[XY,CM,30,3] MI[ZX,CM,T] C[0.40,0,0] PA[XY,CM,30,2]

M[0.40,0,0] R[XY,CM,45] PA[XY,CM,30,3] MI[ZX,CM,T] MI[ZX,CM,T] S2[XY,2,CM,F]

C[0.65,0,0] R[XY,CM,45] PA[XY,CM,30,3] MI[ZX,CM,T] MI[ZX,CM,T] S2[XY,2,CM,F]

C[0.65,0,0] R[XY,CM,45] PA[XY,CM,30,3] MI[ZX,CM,T] MI[ZX,CM,T] S2[XY,2,CM,F]

S1[X,3.0,CM(0.707,0,-0.175,F] PA[YX,CM(0.8,0,0),180, 5] R[ZX,CM,-45] MI[ZX,CM,T] MI[ZX,CM,T] S2[XY,2,CM,F]

C[0.65,0,0] R[XY,CM,45] R[ZX,CM,-45] MI[ZX,CM,T] MI[ZX,CM,T] S2[XY,2,CM,F]

GEN 05.04

S1[X,3.0,CM(0.707,0,-0.175,F] PA[YX,CM(0.8,0,0),180, 5] PA[XY,CM,30,3] MI[ZX,CM,T] MI[ZX,CM,T] 2[XY,2,CM,F]

GEN 05.09

C[0.65,0,0] R[XY,CM,45] PA[XY,CM,30,3] C[0.65,0,0] S2[XY,2,CM,F]

GEN 05.14

S1[X,3.0,CM(0.707,0,-0.175,F] R[XY,CM,45] MI[ZX,CM,T] MI[ZX,CM,T] PA[XY,CM,30,2]

GEN 05.05 C[0.65,0,0] R[XY,CM,45] R[ZX,CM,-45] MI[ZX,CM,T] MI[ZX,CM,T] S2[XY,2,CM,F]

GEN 05.10

S1[X,3.0,CM(0.707,0,-0.175,F] PA[YX,CM(0.8,0,0),180, 5] PA[XY,CM,30,3] MI[ZX,CM,T] PA[XY,CM,30,2]

GEN 05.15

C[0.65,0,0] R[XY,CM,45] PA[XY,CM,30,3] MI[ZX,CM,T] MI[ZX,CM,T] S2[XY,2,CM,F]

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Individual

Surface Area

Volume

SA/V

Individuals Ranked

SA/V (Ordered)

G5-01

11.23

1.23

9.13

G5-02

10.56

G5-02

11.23

1.23

9.13

G5-10

10.48

G5-03

57.41

6.46

8.89

G5-07

10.23

G5-04

93.18

10.31

9.04

G5-09

9.72

G5-05

9.36

1.03

9.09

G5-01

9.60

G5-06

7.98

0.78

10.23

G5-13

9.37

G5-07

16.52

1.16

9.72

G5-04

9.28

G5-08

11.69

1.26

9.28

G5-14

9.16

G5-09

12.83

1.40

9.16

G5-06

9.13

G510

34.33

3.25

10.56

G5-03

9.13

G5-11

10.87

1.16

9.37

G5-05

9.13

G5-12

11.23

1.23

9.13

G5-08

9.09

G5-13

9.36

1.03

9.09

G5-11

9.09

G5-14

12.78

1.22

10.48

G5-15

9.04

G5-15

16.52

1.72

9.60

G5-12

8.89

Sequence 01 Population 05 Fittest Population: The Intention of cross-breeding just the five (5) most fittest brought a population with high standards having almost all its individuals close to the mean line. This automatically discard the posibility of have unfit individuals in terms of perfomance/skills.

Mean Standard Deviation

9.76 1.31

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

G01

G02

G01-01

G02-01 S R MI S1 M

S R MI S1 M

G01-02

G02-02 S2 S C S R

S2 S C S R

G01-03

G02-03 C R S1

C R S1

G01-04

G02-04 R MI S1 PA PA

R MI S1 PA PA

G01-05 T

G02-05

G04

G05

G03-01

G04-01

G05-01

C R R MI C PA

C R PA MI S2

C R R MI C S2

G03-02

G04-02

G05-02

S1 PA C R PA

S1 PA C S2 PA

G03-03

Logic Diagram

C R PAMI C S2

G04-03

G05-03

S1 PAS2 C C

C R C S2 C PA

S1 PA R MI C S2

G03-04

G04-04

G05-04

S1 PA C PAS2

C R R MI C S2

S1 PA PAMI C S2

G03-05

G04-05

G05-05

S1 PA S2

S1 PAC MI C S2

C R R MI C S2

G04-06

G05-06

BN MI R M S

MI R M S

G01-06

BW

G02-06 C R R MI MI C R

C R R MI MI C R

BE

G01-07

G02-07 S1 PA

S1 PA

BS

G03

PRIMITIVE

G02-08

G01-08

C R PA

C R PA

G02-09

G01-09

G03-06 R R MI S2

G03-07 M R R MI MI S1

G03-08 R R MI C R S2

G03-FITTEST A C R R MI MI S1

G03-FITTEST B R R MI C R S2

G03-09

R M PAMI C S2

C R PA C R PA

G04-07

G05-07

C R PAMI C S2

C R PAMI C S2

G04-08

G05-08

R C MI C S2 MUTANT

C R PAMI S2

G04-09

G05-09

Fittest Individual S2 PA

S2 PA

Parent 1

G01-10

G02-10

C PA S2 C S2 PA

R M C PAS2

C R PAMIS2

G03-10

G04-10

G05-10

C R MI R C

C R C PAS2

S1 PAPA MI PA

G03-11

G04-11

G05-11

S2 PA C PA

R M R C S2 PA MUTANT

C R PAMI C S2

G03-12

G04-12

G05-12

C R R C S2

S2 PA R C S2

C R PAMI C S2

MODIFIED

Parent 2

R S1

R S1

G01-11

G02-11 C PA S2

C PA S2

G01-12

G02-12 C S2 PA

C S2 PA

G01-13

G02-13 S1 C R

S1 C R

G01-14

G02-14

G03-13

G04-13

G05-13

PA S2 C C

C R C PA S2

C R R MI C S2

G03-14

G04-14

G05-14

S2 S2 C C

C R C PAS2

S1 R PAMI S2

G03-15

G04-15

G05-15

C PA S2 C

S2 PA R C S2

C R PA MI C S2

MODIFIED S2 C C

G01-15 C S2 R

S2 C C

G02-15 C S2 R

Asexual Reproduction

Sexual Reproduction

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

3 split

Sequence 01

GENES STRUCTURE ANALYSIS SEQUENCE 01

Conclusion

Fittest Populations The implementation of new breeding strategies from generations 3 by: 路The selection of the fittest third of the populations, 路Crossbreeding between the two fittest of the population to create better offsprings, 路The addition of random mutations we managed to create betters population by a evolutionary development.

73 Joined

Genes Structure Analysis The evolution of harmful genes produce a tremendous repercutions on further generations. The use of genes/instructions that allow the original primitive a displacement far from its origin causes a deformation on the final individual. As a critiria of selection, all the individuals who its geometry is split or separated is automatically catalogued as a dead individual by the killing strategies.

75 Individuals (Gen01-Gen05) Split Geometries on individuals

RANDOM CROSSBREEDING

Selection Criteria Sequence

BREEDING STRATEGY IMPLEMENTED

SELECTION CRITERIA 2 MODIFIED

01 GEN01

02 GEN02

1/3 FITTEST REMAIN ALIVE

03

1/3 FITTEST REMAIN ALIVE + A&B (GEN03 FITTEST COMBINATION)

GEN03

04

1/3 FITTEST REMAIN ALIVE

GEN04

05 GEN05

3 MUTANTS ADDED ASEXUAL REPRODUCTION

SEXUAL REPRODUCTION

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Fitness

Sequence 01

Individual Ordered

29

9.28

G05-08

30

9.26

G04-14

31

9.24

G04-06

32

9.16

G05-09

33

9.14

G04-03

34

9.13

G05-02

35

9.13

G05-02

36

9.09

G05-12

37

9.09

G05-05

38

9.04

G05-13

39

9.04

G05-04

40

8.89

G05-03

41

8.86

G01-10

42

8.82

G01-03

59

8.08

G02-01

43

8.82

G2-03

60

8.08

G03-06

44

8.77

G4-05

61

8.06

G03-14

45

8.75

G04-08 MUTANT

62

7.82

G03-08

46

8.68

G01-04

63

7.81

G01-08

47

8.68

G02-04

64

7.73

G02-08

48

8.65

G04-11 MUTANT

65

7.73

G02-14

49

8.63

G03-04

66

7.69

G02-13

50

8.62

G03-15

67

7.60

G04-12

51

8.61

G01-15

68

7.40

G02-10

52

8.61

G02-15

69

7.38

G03-10

53

8.60

G03-05

70

6.86

G01-09

54

8.53

G04-15

71

5.80

G02-09

55

8.37

G03-03

72

5.80

G02-02

56

8.36

G03-09

73

4.41

G01-05

57

8.15

G03-13

74

3.67

G02-05

58

8.15

G01-01

75

3.67

G01-02

1

14.24

G05-07

2

14.06

G01-13

3

11.11

G03-01

4

10.74

G01-06

5

10.74

G01-06

6

10.62

G01-11

7

10.62

G02-11

8

10.56

G5-10

9

10.48

G05-14

10

10.36

G04-02

11

10.23

G05-06

12

9.83

G04-10

13

9.78

G04-07

14

9.72

G01-07

15

9.72

G02-07

16

9.72

G04-09

17

9.67

G03-11

18

9.60

G05-15

19

9.56

G03-02

20

9.56

G01-12

21

9.56

G02-12

22

9.53

G03-07

23

9.52

G04-01

24

9.43

G01-14

25

9.37

G05-11

26

9.36

G04-13 MUTANT

27

9.35

G03-12

28

9.31

G04-04

Conclusion Improvement through evolution: Every living form emerge from a strong coupling process. Small variations produces the the material of variant forms where natural selection acts as the force that chooses the survivor form. the fittest third of each populations reflected that through time that the whole population is enhanced and this is shown in the Generation05(G05/purple color) its located entirely in the first half of the table of fittest individuals.

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

G1-13

14.06

G2-06

10.74

G3-01

11.11

G4-02

10.36

G1-06

10.74

G2-11

10.62

G3-11

9.67

G4-10

9.83

G1-11

10.62

G2-07

9.72

G3-02

9.56

G4-07

9.78

G1-07

9.72

G2-12

9.56

G3-07

9.53

G4-09

9.72

G1-12

9.56

G2-03

8.82

G3-12

9.35

G4-01

9.52

G1-14

9.43

G2-04

8.68

G3-04

8.63

G4-13

MUTANT 9.36

G1-10

8.86

G2-15

8.61

G3-15

8.62

G4-04

9.31

G1-03

8.82

G2-01

8.08

G3-05

8.60

G4-14

9.26

G1-04

8.68

G2-08

7.73

G3-03

8.37

G4-06

9.24

G1-15

8.61

G2-14

7.69

G3-09

8.36

G4-03

9.14

G1-01

8.08

G2-13

7.60

G3-13

8.15

G4-05

8.77

8.06

G1-08

7.73

G2-10

7.38

G3-06

G4-08

MUTANT 8.75

G1-09

5.80

G2-09

5.80

G3-14

7.82

G4-11

MUTANT 8.75

G1-05

3.67

G2-02

4.41

G3-08

7.81

G4-15

8.53

G1-02

3.00

G2-05

3.67

G3-10

6.86

G4-12

7.40

G5-07

14.24

G5-10

10.56

G5-14

10.48

G5-06

10.23

G5-15

9.60

G5-11

9.37

G5-08

9.28

G5-09

9.16

G5-02

9.13

G5-02

9.13

G5-05

9.09

G5-05

9.09

G5-01

9.04

G5-04

9.04

G5-03

8.89

Sequence 01 Fitness Comparison

Population 01 Population 02 Population 03 Population 04 Population 05

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

EMERGENCE SEMINAR Sequence 02

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Sequence 02 Body Plan

Instructions Scale Scale, ScalingFactor, Point, Copy eg. S[1.2,CM,T] Scale 1D Scale, Vector, ScalingFactor, Point, Copy eg. S1[Y,1.2,CM,F] Scale 2D Scale, Plane, ScalingFactor, Point, Copy eg. S2[XY,1.2,CM,F] Mirror Mirror, Plane, Point, Copy (copy true or false) eg. MI[YZ,BN,T] Move Move, Vector eg. M[(7.5,0,0)] Copy Copy, Vector eg. C[(1,0,0)] Rotate Rotate, Plane, Point, Degree eg. R[XY, BE,45] Polar Array PolarArray, Plane, Point, Degree, NumberOfCopies eg. PA[YZ, BW, 15, 5] Points

Body plan structure: The body plan defines details of the individual structure on its body part. Powerful changes in the organizing of genes have tremendous repercussion in the final shape, size and number of parts in every living creature. The emergence phenomena of having small complex changes that arise in even larger more complex system and organizations is built over time by alterations to the existing forms.

T

T, BW, BN, BS, BE, BW, BE, BS, BN, CM, CM’

BN

BW

Fitness Criteria Shadow Area/ Volume

BE BS T

BN

BW CM CM’ BE BS

Shadow

Shadow Direction

Body Plan 02

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

t1

Sequence 02 Breeding Strategy

BREEDING STRATEGIES Parent 1

BREEDING STRATEGIES Parent 1

Parent 2

Descendant Parent 2

Parent 1

Parent 2

Parent 2

Parent 1

Descendant Parent 1

Parent 2

Descendant Parent 1 Parent 2

Parent 1

Descendant

Parent 2

Descendant Parent 1 Parent 2

Parent 2

Parent 1 Parent 2

Descendant Parent 1 Parent 2

Parent 1

Parent 2

Descendant Parent 1 Parent 2

Descendant Parent 1 Parent 2

Parent 1 Parent 2

Parent 1 Parent 2

Descendant

Descendant

Parent 1 Parent 2

Descendant

Parent 1 Parent 2

Descendant

MUTATIONS Insertion Deletion

Sequence_01

escendant

& Descendant Descendant 10 Unfit Individuals 2/3 Killed

Duplication Descendant

Descendant Split Geometries

Descendant

Inversion

Killing Strategy

&

Sequence_02 11 Unfit Individuals Killed

Split Geometries

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Sequence 02 Population 06

GEN 06.01

GEN 06.02

GEN 06.03

GEN 06.06

GEN 06.07

GEN 06.08

S1[Y,1.9,CM,F] R[XY, BN,45] S[0.9,T,F] M[(1,0.1,0.1)] R[YZ, BN,20] PA[XZ, BN, 3,10]

S2[YZ,0.7,CM’,T] R[XZ, BE,55] S1[Y,1.2,BW,T] PA[XZ, BS, 10, 1] S[1.2,CM,T] MI[YZ,BN,T]

GEN 06.11

GEN 06.12

GEN 06.13

S[1.7,BN,F] MI[YZ,BN,T] R[XZ, CM ,60] R[XY, BE,30] R[YZ, BE,45] S1[Y,1.2,CM,F]

R[XY, BN,45] M[(0.25,0,0)] S1[Y,1.2,CM’,T] S[1.2,CM,T] R[XY, BS,35] S[0.9,BE,F]

R[XZ, BW,15] M[(0.5,2,1)] C[(1,1.7,1)] S1[Y,2,BE,T] PA[YZ, BS, 2, 10] R[XY, BW,65]

M[(0.5,0,0.2)] R[YZ, BE,30] MI[YZ,BN,CM’,T] S2[XY,1.2,BN,F] PA[XZ, BW, 3, 20] R[XY, BE,45]

MI[YZ,CM’,F] S1[Y,1.8,BE,F] R[XY, CM,15] C[(1,1.5,2)] S[1.7,BE,T] S2[XZ,1.2,BE,T]

M[(0.2,0,0)] S[1.7,BN,T] PA[XY, T, 2, 30] M[(0.1,0,0.2)] S1[Y,1.2,BS,F] S2[XY,1.9,BW,F]

PA[YX, BE, 20, 5] R[XZ, BS,25] MI[YZ,BE,F] S2[XZ,2.4,CM,T] MI[YX,BS,F] S1[Y,2.7,BW,F]

GEN 06.04

PA[YZ, CM, 2, 10] R[YZ, CM,20] M[(0.4,0.6,0.2)] S[2,T,BE] MI[XY,BS,T] R[XZ, BS,70]

GEN 06.09

S[2,CM’,F] M[(0.5,2,1)] C[(1,3,2)] S2[YZ,.6,CM’,T] R[XY, BS,60] PA[YZ, BN, 2, 20]

GEN 06.14

R[XY, BN, 90] PA[YZ, BN, 120,7] S1[Y,2.4,BW,F] S[2,CM,F] PA[YX, BS, 10, 5] C[(2,6,4)]

GEN 06.05

S[1.7,T,F] S2[XZ,0.7,CM,T] R[XY, BS,90] PA[YZ, BN, 2, 30] MI[YZ,CM’,T] S1[Y,1.9,BN,F]

GEN 06.10

C[(1,0.5,1.2)] S1[Y,1.8,CM’,T] PA[YZ, BN, 7, 2] R[XZ, BW,25] S1[Y,1.8,BS,F] M[(1,2.4,2)]

GEN 06.15

M[(2,1,2)] C[(1,1,2)] R[XY, BN,75] S2[XY,2.6,BS,F] S[2.7,CM’,F] M[(0.5,1,0.5)]

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Individual

Shadow Area

Volume

SA/V

Individuals Ranked

SA/V (Ordered)

G6-01

10.26

1.50

6.84

G6-11

13.52

G6-02

4.64

0.48

9.67

G6-04

12.58

G6-03

12.47

1.29

9.67

G6-10

12.51

G6-04

7.42

0.59

12.58

G6-12

11.95

G6-05

8.69

1.76

4.94

G6-02

9.67

G6-06

2.51

0.28

8.96

G6-03

9.67

G6-07

2.23

0.33

6.76

G6-06

8.96

G6-08

1.98

1.00

1.98

G6-13

8.27

G6-09

23.14

3.31

6.99

G6-14

8.22

G6-10

8.38

0.67

12.51

G6-15

8.22

G6-11

6.49

0.48

13.52

G6-09

8.19

G6-12

7.77

0.65

11.95

G6-01

6.99

G6-13

7.53

0.91

8.27

G6-07

6.76

G6-14

9.21

1.12

8.22

G6-05

4.94

G6-15

32.77

4.00

8.19

G6-08

1.98

Mean Standard Deviation

Sequence 02 Population 06 New fitness criteria, a new start: A new fitness criteria was implemented with the idea of insert a environmental contidion (Shadow Area/Volume).

This new addition insert and architectural approach to the experiment in terms of environmental phenomena.

Displacement studies: A new body plan was added intentionally with the idea of understand the domain of the individual, based on the new horizontal cut in the middle of the body plan and analyzing / controlling the displacement factor.

8.74 3.00

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Sequence 02

RANDOM BREEDING STRATEGY

Population 07

GEN 07.01

GEN 07.02

GEN 07.03

GEN 07.04

GEN 07.05

GEN 07.06

GEN 07.07

GEN 07.08

PA[YZ, CM, 2, 10] R[YZ, CM,20] M[(0.4,0.6,0.2)] S1[Y,2,BE,T] PA[YZ, BS, 2, 10] R[XY, BW,65]

C[(1,0.5,1.2)] S1[Y,1.8,CM’,T] PA[YZ, BN, 2, 7] S1[Y,2,BE,T] PA[YZ, BS, 2, 10] R[XY, BW,65]

GEN 07.09

GEN 07.10

GEN 07.11

GEN 07.12

GEN 07.13

GEN 07.14

GEN 07.15

R[XZ, BW,15] M[(0.5,2,1)] C[(1,1.7,1)] S[7,T,BE] MI[XY,BS,T] R[XZ, BS,70]

R[XZ, BW,15] M[(0.5,2,1)] C[(1,1.7,1)] C[(1,1.5,2)] S[1.7,BE,T] S2[XZ,1.2,BE,T]

C[(1,0.5,1.2)] S1[Y,1.8,BE,F] PA[YZ, BN, 2, 7] C[(1,1.5,2)] S1[Y,1.8,BS,F] S2[XZ,1.2,BE,T]

PA[YZ, CM, 10, 2] R[YZ, CM,20] M[(0.4,0.6,0.2)] R[XZ, BW,25] S1[Y,1.8,BS,F] M[(1,2.4,2)]

R[XZ, BW,15] S1[Y,1.8,CM’,T] C[(1,1.7,1)] R[XZ, BW,25] PA[YZ, BS, 2, 10] M[(1,2.4,2)]

C[(1,0.5,1.2)] S1[Y,1.8,CM’,T] PA[YZ, BN, 2, 7] C[(1,1.5,2)] S[1.7,BE,T] S2[XZ,1.2,BE,T]

PA[YZ, CM, 2, 10] S1[Y,1.8,BE,F] M[(0.4,0.6,0.2)] C[(1,1.5,2)] S1[Y,1.8,BS,F] S2[XZ,1.2,BE,T]

R[XZ, BW,15] M[(0.5,2,1)] C[(1,1.7,1)] R[XZ, BW,25] S1[Y,1.8,BS,F] M[(1,2.4,2)]

R[XZ, BW,15] R[YZ, CM,20] C[(1,1.7,1)] S[2,T,BE] MI[XY,BS,T] R[XZ, BS,70]

R[XZ, BW,15] S1[Y,1.8,BE,F] C[(1,1.7,1)] C[(1,1.5,2)] PA[YZ, BS, 2, 10] S2[XZ,1.2,BE,T]

PA[YZ, CM, 2, 10] R[YZ, CM,20] M[(0.4,0.6,0.2)] C[(1,1.5,2)] S[1.7,BE,T] S2[XZ,1.2,BE,T]

PA[YZ, CM, 2, 10] S1[Y,1.8,CM’,T] M[(0.4,0.6,0.2)] R[XZ, BW,25] MI[XY,BS,T] M[(1,2.4,2)]

PA[YZ, CM, 2, 10] M[(0.5,2,1)] M[(0.4,0.6,0.2)] S1[Y,2,BE,T] MI[XY,BS,T] R[XY, BW,65]

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Individual

Shadow Area

Volume

SA/V

Individuals Ranked

SA/V (Ordered)

G7-01

22.85

3.94

5.80

G7-04

14.80

G7-02

2.51

0.41

6.12

G7-07

13.84

G7-03

9.25

1.68

5.51

G7-05

11.84

G7-04

6.66

0.45

14.80

G7-08

11.58

G7-05

7.46

0.63

11.84

G7-13

9.17

G706

3.11

0.63

4.94

G7-15

9.08

G7-07

6.09

0.44

13.84

G7-10

7.44

G7-08

8.34

0.72

11.58

G7-02

6.12

G7-09

3.69

0.82

4.50

G7-01

5.80

G7-10

6.25

0.84

7.44

G7-03

5.51

G7-11

2.58

0.80

3.23

G7-14

5.13

G7-12

3.31

0.67

4.94

G7-12

4.94

G7-13

4.86

0.53

9.17

G7-06

4.94

G7-14

3.13

0.61

5.13

G7-09

4.50

G7-15

5.36

0.59

9.08

G7-11

3.23

Mean Standard Deviation

7.86 3.53

Mean Standard Deviation Breeding Strategy

9.30 3.85

Sequence 02 Population 07 New Selection Criteria: The selection criteria was reduced to only four(4) fittest individuals to narrow up the posibilities of cross-over in each gene.

Breeding strategy comparison: The comparision in between three(3) different breeding criterias were analizad with the intention of seen how the new Offspring were affected due to the positioning of its genes.

Mean Standard Deviation

6.21 2.18

Breeding Strategy

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Sequence 02

Population 07A

GEN 07A.01 R[XZ, BW,15] M[(0.5,2,1)] C[(1,1.7,1)] S[7,T,BE] MI[XY,BS,T] R[XZ, BS,70]

GEN 07A.02

PA[YZ, CM, 10, 2] R[YZ, CM,20] M[(0.4,0.6,0.2)] R[XZ, BW,25] S1[Y,1.8,BS,F] M[(1,2.4,2)]

GEN 07A.03

GEN 07A.04

C[(1,0.5,1.2)] S1[Y,1.8,CM’,T] PA[YZ, BN, 2, 7] C[(1,1.5,2)] S[1.7,BE,T] S2[XZ,1.2,BE,T] PA[YZ, CM, 2, 10]

GEN 07A.05

R[XZ, BW,15] M[(0.5,2,1)] C[(1,1.7,1)] R[XZ, BW,25] S1[Y,1.8,BS,F] M[(1,2.4,2)]

PA[YZ, CM, 2, 10] R[YZ, CM,20] M[(0.4,0.6,0.2)] C[(1,1.5,2)] S[1.7,BE,T] S2[XZ,1.2,BE,T]

Mutated

GEN 07A.06 R[XZ, BW,15] M[(0.5,2,1)] C[(1,1.7,1)] C[(1,1.5,2)] S[1.7,BE,T] S2[XZ,1.2,BE,T]

GEN 07A.07

PA[YZ, CM, 2, 10] R[YZ, CM,20] M[(0.4,0.6,0.2)] S1[Y,2,BE,T] PA[YZ, BS, 2, 10] R[XY, BW,65]

GEN 07A.08

C[(1,0.5,1.2)] S1[Y,1.8,CM’,T] PA[YZ, BN, 2, 7] S1[Y,2,BE,T] PA[YZ, BS, 2, 10] R[XY, BW,65]

GEN 07A.09

R[XZ, BW,15] R[YZ, CM,20] C[(1,1.7,1)] S[2,T,BE] MI[XY,BS,T] MI[XY,BS,T] R[XZ, BS,70]

GEN 07A.10

PA[YZ, CM, 2, 10] S1[Y,1.8,CM’,T] M[(0.4,0.6,0.2)] R[XZ, BW,25] MI[XY,BS,T] M[(1,2.4,2)]

Mutated

GEN 07A.11

C[(1,0.5,1.2)] S1[Y,1.8,BE,F] PA[YZ, BN, 2, 7] C[(1,1.5,2)] S1[Y,1.8,BS,F] S2[XZ,1.2,BE,T]

GEN 07A.12

R[XZ, BW,15] S1[Y,1.8,CM’,T] C[(1,1.7,1)] R[XZ, BW,25] PA[YZ, BS, 2, 10] M[(1,2.4,2)]

GEN 07A.13

PA[YZ, CM, 2, 10] S1[Y,1.8,BE,F] M[(0.4,0.6,0.2)] DELETED S1[Y,1.8,BS,F] S2[XZ,1.2,BE,T]

GEN 07A.14

R[XZ, BW,15] S1[Y,1.8,BE,F] C[(1,1.7,1)] C[(1,1.5,2)] PA[YZ, BS, 2, 10] S2[XZ,1.2,BE,T]

GEN 07A.15

PA[YZ, CM, 2, 10] M[(0.5,2,1)] S1[Y,2,BE,T] M[(0.4,0.6,0.2)] MI[XY,BS,T] R[XY, BW,65]

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Individual

Shadow Area

Volume

SA/V

Individuals Ranked

SA/V (Ordered)

G7A-01

22.85

3.94

5.80

G7A-13 DELETION

15.75

G7A-02

2.51

0.41

6.12

G7A-04

14.80

G7A-03

4.24

1.04

4.08

G7A-07

13.84

G7A-04

6.66

0.45

14.80

G7A-05

11.84

G7A-05

7.46

0.63

11.84

G7A-08

11.58

G7A-06

3.11

0.63

4.94

G7A-10

7.44

G7A-07

6.09

0.44

13.84

G7A-02

6.12

G7A-08

8.34

0.72

11.58

G7A-15 INVERTION

5.82

G7A-09

4.16

1.03

4.04

G7A-01

5.80

G7A-10

6.25

0.84

7.44

G7A-14

5.13

G7A-11

2.58

0.80

3.23

G7A-12

4.94

G7A-12

3.31

0.67

4.94

G7A-06

4.94

G7A-13

6.30

0.40

15.75

G7A-03 INSERTION

4.08

G7A-14

3.13

0.61

5.13

G7A-09 DUPLICATION

4.04

G7A-15

3.20

0.55

5.82

G7A-11

3.23

Mean Standard Deviation

7.96 4.18

Mean Standard Deviation Breeding Strategy

8.59 4.79

Sequence 02 Population 07A More mutations: Four (4) mutants were added in the Generation07A (G07A), comparing if by the addition of the same amount of fittest individuals, the decendants reacts different.

Breeding strategy comparison: The comparision in between three( 3) different breeding criterias were analized with the intention of seen how the new offspring were affected due to the positioning of its genes.

Mean Standard Deviation

6.62 3.93

Breeding Strategy

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Sequence 02 Population 08

GEN 08.01

GEN 08.02

GEN 08.03

GEN 08.06

GEN 08.07

GEN 08.08

R[XZ, BW,15] M[(0.5,2,1)] C[(1,1.7,1)] S1[Y,1.8,BS,F] S2[XZ,1.2,BE,T] M[(1,2.4,2)]

PA[YZ, CM, 2, 10] R[YZ, CM,20] M[(0.4,0.6,0.2)] S1[Y,1.8,BS,F] S2[XZ,1.2,BE,T] R[XY, BW,65]

GEN 08.11

GEN 08.12

GEN 08.13

PA[YZ, CM, 2, 10] S1[Y,1.8,BE,F] M[(0.4,0.6,0.2)] R[XZ, BW,25] S1[Y,1.8,BS,F]

R[XZ, BW,15] M[(0.5,2,1)] C[(1,1.7,1)] S1[Y,2,BE,T] PA[YZ, BS, 2, 10] R[XY, BW,65]

M[(1,2.4,2)]

PA[YZ, CM, 2, 10] S1[Y,1.8,BE,F] M[(0.4,0.6,0.2)] C[(1,1.5,2)] S[1.7,BE,T] S2[XZ,1.2,BE,T]

PA[YZ, CM, 2, 10] R[YZ, CM,20] M[(0.4,0.6,0.2)] C[(1,1.5,2)] PA[YZ, BS, 2, 10] S2[XZ,1.2,BE,T]

PA[YZ, CM, 2, 10] R[YZ, CM,20] M[(0.4,0.6,0.2)] S1[Y,2,BE,T] S2[XZ,1.2,BE,T] R[XY, BW,65]

PA[YZ, CM, 2, 10] R[YZ, CM,20] M[(0.4,0.6,0.2)] C[(1,1.5,2)] S[1.7,BE,T] S2[XZ,1.2,BE,T]

R[XZ, BW,15] R[YZ, CM,20] C[(1,1.7,1)] C[(1,1.5,2)] S1[Y,1.8,BS,F] S2[XZ,1.2,BE,T]

GEN 08.04

PA[YZ, CM, 2, 10] S1[Y,1.8,BE,F] M[(0.4,0.6,0.2)] S1[Y,2,BE,T] PA[YZ, BS, 2, 10] R[XY, BW,65]

GEN 08.09

PA[YZ, CM, 2, 10] M[(0.5,2,1)] M[(0.4,0.6,0.2)] R[XZ, BW,25] S2[XZ,1.2,BE,T] M[(1,2.4,2)]

GEN 08.14

PA[YZ, CM, 2, 10] R[YZ, CM,20] M[(0.4,0.6,0.2)] C[(1,1.5,2)] S2[XZ,1.2,BE,T] S2[XZ,1.2,BE,T]

GEN 08.05 R[XZ, BW,15] M[(0.5,2,1)] C[(1,1.7,1)] C[(1,1.5,2)] S[1.7,BE,T] S2[XZ,1.2,BE,T]

GEN 08.10

R[XZ, BW,15] R[YZ, CM,20] C[(1,1.7,1)] S1[Y,2,BE,T] PA[YZ, BS, 2, 10] R[XY, BW,65]

GEN 08.15 R[XZ, BW,15] S1[Y,1.8,BE,F] C[(1,1.7,1)] S1[Y,1.8,BS,F] S1[Y,1.8,BS,F] M[(1,2.4,2)]

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Individual

Shadow Area

Volume

SA/V

Individuals Ranked

SA/V (Ordered)

G8-01

3.37

0.60

5.62

G8-08

15.70

G8-02

2.70

0.50

5.40

G8-04

14.12

G8-03

3.44

0.63

5.46

G8-12

10.35

G8-04

9.46

0.67

14.12

G8-11

9.45

G8-05

3.21

0.62

5.18

G8-14

8.30

G8-06

4.62

0.78

5.92

G8-10

6.86

G8-07

2.52

0.47

5.36

G8-06

5.92

G8-08

6.91

0.44

15.70

G8-15

5.81

G8-09

1.81

0.32

5.66

G8-13

5.69

G8-10

3.36

0.49

6.86

G8-09

5.66

G8-11

5.01

0.53

9.45

G8-01

5.62

G8-12

6.21

0.60

10.35

G8-03

5.46

G8-13

2.96

0.52

5.69

G8-02

5.40

G8-14

4.73

0.57

8.30

G8-07

5.36

G8-15

4.82

0.83

5.81

G8-05

5.18

Mean Standard Deviation

7.66 3.24

Mean Standard Deviation Breeding Strategy

7.85 4.10

Sequence 02 Population 08 Breeding strategy comparison: The comparision in between three( 3) different breeding criterias were analized with the intention of seen how the new offspring were affected due to the positioning of its genes.

Mean Standard Deviation

7.44 1.79

Breeding Strategy

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Individual

Shadow Area

Volume

SA/V

Individuals Ranked

SA/V (Ordered)

G8A-01

3.37

0.60

5.62

G8A-08

15.70

G8A-02

2.70

0.50

5.40

G8A-04

14.12

G8A-03

3.44

0.63

5.46

G8A-12

10.35

G8A-04

9.46

0.67

14.12

G8A-11

9.45

G8A-05

3.03

0.60

5.05

G8A-14

8.30

G8A-06

4.62

0.78

5.92

G8A-10

6.86

G8A-07

2.55

0.27

9.44

G8A-06

5.92

G8A-08

6.91

0.44

15.70

G8A-15

5.81

G8A-09

1.81

0.32

5.66

G8A-13

5.69

G8A-10

3.36

0.49

6.86

G8A-09

5.66

G8A-11

7.84

0.49

9.45

G8A-01

5.62

G8A-12

2.74

0.62

12.65

G8A-03

5.66

G8A-13

2.96

0.52

5.69

G8A-02

5.40

G8A-14

4.73

0.57

8.30

G8A-07

5.36

G8A-15

4.82

0.83

5.81

G8A-05

5.18

Mean Standard Deviation

7.83 3.41

Mean Standard Deviation Breeding Strategy

8.34 4.03

Sequence 02 Population 08A More mutations: Four (4) mutants were added in the Generation08A (G08A), comparing if by the addition of the same amount of fittest individuals, the decendants reacts different.

Breeding strategy comparison: The comparision in between three (3) different breeding criterias were analizad with the intention of seen how the new Offspring were affected due to the positioning of its genes.

Mean Standard Deviation

7.24 2.38

Breeding Strategy

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Sequence 02 Generation Comparaison

M: 8.74 SD: 3.00 M: 7.86 SD: 3.56 M: 7.86 SD: 4.18 M: 7.66 SD: 3.24 M: 7.83 SD: 3.14

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

G06

G07

G06-01

G07-01

G07A

G08 G08-01

S MI R R R S1

R M C S MI R

PAS1 M R S1 M

G06-02

G07-02

G08-02

M R MIS2 PA R

PA R M R S1 M

G06-03

G07-03

Sequence 02

Logic Diagram

R M C S1 PA R

G07-03

G08-03

S1 PA R MI C S2 PA

PA R M C S S2

MUTATION M S PA M S1 S2

C S1PA C S S2

G06-04

G07-04

G08-04

PA R M S MI R

R M C R S1 M

PAS1 M S1 PA R

G06-05

G07-05

G08-05

S S2 R PAMI S1

R M C C S S2

R M C C S S2

G06-06

G07-06

G08-06

T BN

BW

BE BS T

BN

BW CM

BE

Fittest Individual Parent 1

PA R MS1 PA R

PAS1 M C S S2

G07-07

G08-07

S1 R S M R PA

PA R M S1 PA R

R M C S1 S2 M

G06-08

G07-08

G08-08

S2 R S1 PA S MI

C S1 PAS1 PA R

G06-09

G07-09

PA R M S1 S2 R

G07-09

G08-09

R R C S MI R

PAM M R S2 M

MUTATION

CM’

BS

R M S1 S R S

G06-07

S M C S2 R PA

R R C S MI R

G06-10

G07-10

G08-10

C S1PA R S1 M

PA S1 M R MI M

R R C S1 PA R

G06-11

G07-11

G08-11

R M C S1 PA R

C S1 PA C S1 S2

PA R M C PAS2

G06-12

G07-12

G08-12

MI S1 R C S S2

R S1 C R PA M

G06-13

G07-13

PA R M S1 S2 R

G07-13

G08-13

PAS1 M C S1 S2

R R C C S1 S2

MUTATION

Parent 2 PA R MI S2 MI S1

PAS1 M C S1 S2

G06-14

G07-14

R PAS1 S PA C

R S1 C C PAS2

G06-15

G07-15

G08-14 PA R M C S2 S2

G07-15

G08-15

PA M M S1 MI R

R S1 C S1 S1 M

MUTATION M C R S2 S M

PA MM S1 MI R

Sexual Reproduction

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

GENES STRUCTURE ANALYSIS SEQUENCE 02 Genes Structure Analysis The evolution of harmful genes produce a tremendous repercutions on further generations. The use of genes/instructions that allow the original primitive a displacement far from its origin causes a deformation on the final individual. As a critiria of selection, all the individuals who its geometry is split or separated is automatically catalogued as a dead individual by the killing strategies.

23 Joined

Sequence 02 Conclusion

Primitive Domain The implementation of displacement instructions to the original primitive most be controlled by the area covered on the footprint of it. So, as soon of one or two different instructions(move or copy) are assign, they must be related by this domain by fractions and in this way the factor of displacement never overpass the limits of the primitive and so avoid split/separated geometries.

22 Split

45 Individuals (Gen06-Gen08) Split Geometries on individuals

Entwined Breeding In Generation06 to Generation08 the use of alternated breeding between genomes in the second half of the population create a elevated amount of split geometries in the individuals.

Most of the time the frequence of this deformations were presented 6/7 times.

Parent 1

Parent 2

Descendant SELECTION CRITERIA

06 GEN06

4 FITTEST REMAIN ALIVE

07

4 FITTEST REMAIN ALIVE

GEN07

08 GEN08

Selection Criteria Sequence

4 MUTANTS ADDED SEXUAL REPRODUCTION

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

EMERGENCE SEMINAR Sequence 03

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Sequence 03 Body Plan Body plan structure: Instructions Scale Scale, ScalingFactor, Point, Copy eg. S[1.2,CM,T] Scale 1D Scale, Vector, ScalingFactor, Point, Copy eg. S1[Y,1.2,CM,F] Scale 2D Scale, Plane, ScalingFactor, Point, Copy eg. S2[XY,1.2,CM,F] Mirror Mirror, Plane, Point, Copy (copy true or false) eg. MI[YZ,BN,T] Move Move, Vector eg. M[(7.5,0,0)] Copy Copy, Vector eg. C[(1,0,0)] Rotate Rotate, Plane, Point, Degree eg. R[XY, BE,45] Polar Array PolarArray, Plane, Point, Degree, NumberOfCopies eg. PA[YZ, BW, 15, 5]

The body plan defines details of the individual structure on its body part. Powerful changes in the organizing of genes have tremendous repercussion in the final shape, size and number of parts in every living creature. The emergence phenomena of having small complex changes that arise in even larger more complex system and organizations is built over time by alterations to the existing forms.

T

Points

CM, BN, BE, BS, BW, T, CM’

BN

BW CM CM’

Fitness Criteria

BE

Surface Area/ Volume BS

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

BREEDING STRATEGY

Parent 1

FF

Sequence 03

Parent 2

Breeding Strategy

AA UU

Descendant Parent 1

Parent 2

Parent 1

Parent 2

FA FU AF AU

Descendant Parent 1

Parent 2

Descendant Parent 1 Parent 2

UA UF Descendant

Descendant

Killing Strategy F

A

U

&

Sequence_03

Split Geometries

6 Individuals Killed

F

A

U

Emergent Technologies and Design EMERGENCE SEMINAR

&

Sequence_03 6 Individuals Killed

Split Geometries

Group 03 | 2012-2013

Sequence 03 Population 09

RANDOM BREEDING STRATEGY

Unfittest

GEN 09.01

GEN 09.02

S2[XY,2,CM’,F] S1[Z,5,CM’,F] PA[ZY,CM,3,45]

Fittest

GEN 09.06 S[Z,2,CM’,F] C[(1,0,0)] R[ZY,T,45] C[(1,0,0)] R[ZY,T,45]

GEN 09.03

S1[Y,3,B3,F] R[YZ,BE,-45] MI[ZX,T,T]

PA[ZY,BE,5,360] S2[XZ,2,BW,F] MI[XY,BS,T]+ R[ZY,BS,-45]

GEN 09.04 M[(-.05,0,0)] MI[XY,BE,T] R[XY,BW,90] S2[2,BE,F] C[(0,0-5,0-5)] S1[Y,3,BS,F]

GEN 09.05

C[(0,0-5,0)] S1[Z,2,CM’,F] C[(0.5,0,0.5)] R[ZX,CM’,-60] PA[XY,BN,5,360]

Average

GEN 09.07

S[XY,2,BS,F] C[(0,2,0.25)] MI[XY,BN,T] PA[ZX,BS,5,180]

GEN 09.08

S1[Z,10,CM’,F] C[(0.25,0,0-25)] MI[ZX,BE,T]

GEN 09.09

R[XY, BW, -45] S1[YZ,3,BE,F] PA[ZX,BE,2,45] MI[ZX,BS,F]

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Individual

Shadow Area

Surface Area

SA/V

Individuals Ranked

SA/V (Ordered)

Individual Legend

G09-01

31.92

38.68

0.83

G09-06

1.13

F

G09-02

9.49

9.49

0.96

G09-04

1.12

G09-03

21.01

28.29

0.74

G09-02

0.96

G09-04

24.42

21.85

1.12

G09-05

0.92

G09-05

24.16

26.37

0.92

G09-07

0.91

G09-06

38.69

34.24

1.13

G09-08

0.89

G09-07

40.04

44.15

0.91

G09-09

0.86

G09-08

43.51

48.68

0.89

G09-01

0.83

G09-09

47.27

55.03

0.86

G09-03

0.74

Mean Standard Deviation

Sequence 03 Population 09

A

U

Balancing: 0.93 0.12 The arrangment of genes inside each genome leads to a completely new set of individuals inside a generation. These ones change of position from Fittest, Average and Unfitted finally to a mean tendency.

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Sequence 03 Population 10

Fittest

GEN 10.01 G10-01 C[(1,0,0)] R[ZY,T,45] S[Z,2,CM’,F] C[(1,0,0)] R[ZY,T,45]

GEN 10.02

GEN 10.03

S[Z,2,CM’,F] C[(1,0,0)] R[ZY,T,45] MI[XY,BN,T] PA[ZX,BS,5,180]

Average

GEN 10.06 S[XY,2,BS,F] C[(0,2,0.25)] MI[XY,BS,T] R[ZY,BS,-45]

GEN 10.07

PA[ZY,BE,5,360] S2[XZ,2,BW,F] R[ZY,T,45] C[(1,0,0)] R[ZY,T,45]

GEN 10.04

S[Z,2,CM’,F] C[(1,0,0)] R[ZY,T,45] MI[XY,BS,T] R[ZY,BS,-45]

S[XY,2,BS,F] C[(0,2,0.25)] R[ZY,T,45] C[(1,0,0)] R[ZY,T,45]

GEN 10.05

MI[XY,BN,T] PA[ZX,BS,5,180] S[XY,2,BS,F] C[(0,2,0.25)]

Unfittest

GEN 10.08

PA[ZY,BE,5,360] S2[XZ,2,BW,F] MI[XY,BN,T] PA[ZX,BS,5,180]

GEN 10.09

MI[XY,BS,T] R[ZY,BS,-45] PA[ZY,BE,5,360] S2[XZ,2,BW,F]

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Individual

Shadow Area

Surface Area

SA/V

Individuals Ranked

SA/V (Ordered)

Combinations

G10-01

23.98

19.17

1.25

G10-02

15.70

FA

G10-02

34.06

21.45

1.59

G10-01

14.12

FF

G10-03

5.84

16.39

0.36

G10-05

10.35

AA

G10-04

34.14

37.28

0.92

G10-06

9.45

AU

G10-05

24.82

22.96

1.08

G10-07

8.30

UF

G10-06

31.42

31.93

0.98

G10-04

6.86

AF

G10-07

16.77

17.48

0.96

G10-09

5.92

UU

G10-08

1.08

52.32

0.02

G10-03

5.81

FU

G10-09

7.18

20.55

0.35

G10-08

5.69

UA

Sequence 03 Population 10

It keep balancing: Mean Standard Deviation

0.83 0.47

Pairing these three individuals(fittest,average and unfittes) the tendency reflects that it tends to get closer to the mean to finally get and average population.

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Sequence 03 Population 11

Average

GEN 11.01

GEN 11.02

MI[XY,BN,T] PA[ZX,BS,5,180] S[Z,2,CM’,F] C[(1,0,0)] R[ZY,T,45]

Fittest

GEN 11.06

PA[ZY,BE,5,360] S2[XZ,2,BW,F] R[ZY,T,45] MI[XY,BN,T] PA[ZX,BS,5,180]

GEN 11.03

S[Z,2,CM’,F] C[(1,0,0)] R[ZY,T,45] C[(1,0,0)] R[ZY,T,45]

S[Z,2,CM’,F] C[(1,0,0)] R[ZY,T,45] MI[XY,BN,T] PA[ZX,BS,5,180]

GEN 11.04

PA[ZY,BE,5,360] S2[XZ,2,BW,F] R[ZY,T,45] MI[XY,BN,T] PA[ZX,BS,5,180]

GEN 11.05

C[(1,0,0)] R[ZY,T,45] PA[ZY,BE,5,360] S2[XZ,2,BW,F] R[ZY,T,45]

Unfittest

GEN 11.07

PA[ZY,BE,5,360] S2[XZ,2,BW,F] R[ZY,T,45] MI[XY,BN,T] PA[ZX,BS,5,180]

GEN 11.08

PA[ZY,BE,5,360] S2[XZ,2,BW,F] R[ZY,T,45] C[(1,0,0)] R[ZY,T,45]

GEN 11.09

MI[XY,BN,T] PA[ZX,BS,5,180] PA[ZY,BE,5,360] S2[XZ,2,BW,F]

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Individual

Shadow Area

Surface Area

Sh/SA

Individuals Ranked

Sh/SA (Ordered)

Combinations

G11-01

31.78

25.82

1.23

G11-06

1.55

AU

G11-02

32.82

28.39

1.16

G11-01

1.23

FF

G11-03

21.38

41.50

0.52

G11-04

1.21

AF

G11-04

19.08

52.32

0.36

G11-02

1.16

FA

G11-05

7.52

20.39

0.37

G11-03

0.52

FU

G11-06

39.09

25.20

1.55

G11-05

0.37

AA

G11-07

6.39

25.20

0.25

G11-09

0.36

UU

G11-08

6.46

17.82

0.36

G11-08

0.36

UA

G11-09

25.48

21.02

1.21

G11-07

0.25

UF

Sequence 03 Population 11

Double mutations: Mean Standard Deviation

0.78 0.47

By the addition of double rules of mutation on two offsprings out of the (F, A, U) the generation perform in a differently. It presented a clear increases in the mean numbers what its reflected as an improvement of individuals in the whole populations.

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Sequence 03 Population 11A

Average

GEN 11A.01

GEN 11A.02

MI[XY,BN,T] PA[ZX,BS,5,180] S[Z,2,CM’,F] C[(1,0,0)] R[ZY,T,45]

Fittest

GEN 11A.06 PA[ZY,BE,5,360] S2[XZ,2,BW,F] R[ZY,T,45] MI[XY,BN,T] PA[ZX,BS,5,180]

GEN 11A.03

S[Z,2,CM’,F] C[(1,0,0)] R[ZY,T,45] C[(1,0,0)] R[ZY,T,45]

S[Z,2,CM’,F] C[(1,0,0)] R[ZY,T,45] MI[XY,BN,T] PA[ZX,BS,5,180]

GEN 11A.04 PA[ZY,BE,5,360] S2[XZ,2,BW,F] S2[XZ,2,BW,F] R[ZY,T,45] PA[ZX,BS,5,180]

GEN 11A.05 C[(1,0,0)] R[ZY,T,45] PA[ZY,BE,5,360] S2[XZ,2,BW,F] R[ZY,T,45]

Unfittest

GEN 11A.07 PA[ZY,BE,5,360] S2[XZ,2,BW,F] R[ZY,T,45] MI[XY,BN,T] PA[ZX,BS,5,180]

GEN 11A.08 PA[ZY,BE,5,360] S2[XZ,2,BW,F] R[ZY,T,45] C[(1,0,0)] R[ZY,T,45]

GEN 11A.09 MI[XY,BN,T] S[Z,2,CM’,F] PA[ZX,BS,5,180] S2[XZ,2,BW,F] PA[ZY,BE,5,360]

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Individual

Shadow Area

Surface Area

Sh/SA

Individuals Ranked

Sh/SA (Ordered)

Combinations

G11A-01

31.78

25.82

1.23

G11A-06

1.55

AU

G11A-02

32.82

28.39

1.16

G11A-01

1.23

FF

G11A-03

21.38

41.50

0.52

G11A-04

1.21

AF

G11A-04 MUTANT

19.08

52.32

0.36

G11A-02

1.16

FA

G11A-05

7.52

20.39

0.37

G11A-03

0.52

FU

G11A-06

39.09

25.20

1.55

G11A-05

0.37

AA

G11A-07

6.39

25.20

0.25

G11A-09

0.36

UU

G11A-08

6.46

17.82

0.36

G11A-08

0.36

UA

G11A-09 MUTANT

25.48

21.02

1.21

G11A-07

0.25

UF

Mean Standard Deviation

Sequence 03 Population 11A

0.93 0.12

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Sequence 03 Logic Diagram

T

BN

BW

G09

G10

G11

G09-01

G10-01

G11-01

S2 S1 PA

C R S C R

MI PA S C R

G09-02

G10-02

G11-02

S1 R MI

S C R MI PA

S C R C R

G09-03

G10-03

G11-03

PA S2MI R

S1 C R MI R

S C R MI PA

G09-04

G10-04

G11-04

G11A

MUTATION

BE BS

M MI R S2 C S1

S C R C R

PAS2 R MI PA

G09-05

G10-05

G11-05

C S1 C R PA

MI PA S C

C R PA S2 R

PAS2 S2 MI R PA

PRIMITIVE G09-06

Fittest Individual Average Individual Unfittest Individuals Parent 1

G11-04

G10-06

G11-06

S C RC R S

S C MI R

PAS2 R MI PA

G09-07

G10-07

G11-07

S C MI PA

PAS2 R C R

PAS2 R MI PA

G09-08

G10-08

G11-08

S2 C MI

G09-09

PAS2 MIPA

G10-09

PAS2 R C R

G11 -09

Parent 2

G11 -09 MUTATION

R S1 PAMI

MI R PA S2

MI PA PA S2

MI S1 PA S2 PA

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

G09

G10

G11

FA

F

AU

FF

FF

AA

AF

AU

FA

UF

FU

AF

AA

UU

UU

FU

UA

UA

UF

A

U

Sequence 03 Evolution of the Individuals

MEAN LINE

Remain Position Increased position Decreased Position

DUPLICATION GEN 04.08

GEN 04.08 R[XY,CM,45] C[0.65,0,0] MI[ZX,CM,T] C[0.40,0,0] S2[XY,2,CM,F]

R[XY,CM,45] C[0.65,0,0] MI[ZX,CM,T] C[0.40,0,0] S2[XY,2,CM,F] S2[XY,2,CM,F]

MUTATION TREND R

F

C

MI C

S2

R

Original G11-06 Genome

C

MI C

G11-04

G11-01

MUTATION

FC=1.21

G11-04

R[XY,CM,45] C[0.65,0,0] R[ZX,CM,-45] C[(1,0,0)] S2[XY,2,CM,F]

G11-03 G11-09 R C R C

MEAN LINE GEN 04.11

G11 -09

Original

MI PA PA S2

G11-07

Mutation Typologies

Insertion

G11A-09

INSERTION

S2

Mutation Trend

PAS2 S2 MI R PA

R

G11A-09 FC=0.36

C

R

C

MUTATION

S2 PA

Mutated Genome

G11-08 Genome U

G11A-04

G11A-04

G11-05

GEN 04.11

FC=0.73

PAS2 R MI PA

G11-02 A

S2 S2

Mutated Genome

FC=0.74

R[XY,CM,45] C[0.65,0,0] R[ZX,CM,-45] C[(1,0,0)] S2[XY,2,CM,F] PA[XY,CM,30,3] MI S1 PA S2 PA

Deletion Duplication Inversion

Mutations with tendency to the mean The insertion of a double mutation strategy to a selection of two random numbers in the upper range and lower range showed a decrease in fitness to the individual located in the upper range, while in contrast the individual in the lower range presented an increase in position. Both heading to the mean as a common.

*FC = Fitness Criteria

DUPLICATION GEN 04.13 C[0.65,0,0] R[XY,CM,45] PA[XY,CM,30,3] MI[ZX,CM,T] S2[XY,2,CM,F]

GEN 04.13

C

R

PA MI S2

Original

C

R

MI PA S2

Mutated

C[0.65,0,0] R[XY,CM,45] MI[ZX,CM,T] PA[XY,CM,30,3] S2[XY,2,CM,F]

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

(Gen09-Gen11)

The fittest (F) epiphany The location of a fittest individual combined with an average(A), the unfittest(U) or even the fittest(F) does not assure a enhancement in the individual evolution. It is due to the positioning of the genes inside the new offspring.

Split Geometries on individuals GENES STRUCTURE ANALYSIS SEQUENCE 03

3 split

24 Joined

3

27 Individuals (Gen09-Gen11)

Primitive Domain A contolled critiria of the displacement instructions/commands as Move and Copy is implemented adding to these the posibility of movement up to the scope of footprint, using the latter as the factor of 1. So with this decision we constrain the geometries be separated geometries by the construction of each genome Mean Tendency The selection of the fittest individual(F), the average(A) and the unfittest (U) was a selection critiria in this final experiment.

The combination of all the posibilities of these three create 9 new individuals with some of the characteristics and genes inheritated from their parents. The generation 10 & 11 showed a trend of making every individual goes to the mean value. That means that the whole populations will be in the future an average population overall when its continuous evolving.

The fittest (F) epiphany The location of a fittest individual combined with an average(A), the unfittest(U) or even the fittest(F) does not assure a enhancement in the individual evolution. It is due to the positioning of the genes inside the new offspring.

Split Geometries on individuals

Position Matters The especific location of each gene inside the structure of a genome plays a fundamental roll at the moment of the new sprout comes alive. The information transfered by its parents is crucial not only by the selection of its parents, but also by the arrangement of the genes inside the new children.

Parent 1

Sequence 03 Conclusion

Parent 2

Descendant *Genes selection criteria for the Fit to Fit(FF), Average to Average(AA) and Unfit to Unfit(UU) combinations.

Position Matters The especific location of each gene inside the structure of a genome plays a fundamental roll at the moment of the new sprout comes alive. The information transfered by its parents is crucial not only by the selection of its parents, but also by the arrangement of the genes inside the new children.

Parent 2CRITERIA Parent 1 SELECTION

09 GEN09

MOST FITTEST 路 AVERAGE 路 MOST UNFIT

10

MOST FITTEST 路 AVERAGE 路 MOST UNFIT

Descendant

GEN10 *Genes selection criteria for the Fit to Fit(FF), Average to Average(AA) and Unfit to Unfit(UU) combinations.

TENDENCY OF MEAN POPULATION

Selection Criteria Sequence

2MUTANTS ADDED

11

11A

GEN11

GEN11A

TENDENCY OF MEAN MUTANTS

SEXUAL REPRODUCTION

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Computational Study

vector = (1,0,0),(0,1,0),(0,0,1),(1,1,0),(1,0,1),(0,1,1),(1,1,1),(-1,0,0),(0,-1,0),(-1,1,0),(1,-1,0),(-1,-1,0),(-1,0,1),(0,-1,1) values = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2,1.3,1.4] angles = [2.5,5,7.5,10,12.5,15,17.5,20,25,30,35,40,45,50] angle = (angles*slider_input) modified vector = vector from the list (slider 1) * values (slider 2)

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Computation Fittest Population

Fitness = SC

Fitness = SC+SA

Fitness = SC+SA-V

Fitness = SC+SA-V+H

Fitness = SC

Fitness = SC+SA

Genes = 3 Shadow = 45.17 S. Area = 114.34 Volume = 40.17 Height = 6.28

Genes = 3 Shadow = 61.33 S. Area = 152.2 Volume = 77.22 Height = 8.24

Genes = 3 Shadow = 43.97 S. Area = 111.91 Volume = 40.13 Height = 6.29

Genes = 3 Shadow = 39.53 S. Area = 123.55 Volume = 42.36 Height = 8.11

Genes = 5 Shadow = 41.01 S. Area = 105.05 Volume = 48.04 Height = 5.6

Genes = 5 Shadow = 32.72 S. Area = 103.54 Volume = 50.97 Height = 6.04

Fitness = SC+SA-V

Fitness = SC+SA-V+H

Fitness = SC

Fitness = SC+SA

Fitness = SC+SA-V

Fitness = SC+SA-V+H

Genes = 5 Shadow = 31.65 S. Area = 82.14 Volume = 38.39 Height = 5.28

Genes = 5 Shadow = 18.86 S. Area = 53.47 Volume = 19.1 Height = 4.5

Genes = 9 Shadow = 104.26 S. Area = 313.52 Volume = 185.55 Height = 11.06

Genes = 9 Shadow = 152.3 S. Area = 394.19 Volume = 465.48 Height = 8.5

Genes = 9 Shadow = 39.24S. Area = 98.15 Volume = 98.15 Height = 6.44

Genes = 9 Shadow = 42.27 S. Area = 104.36 Volume = 38.31 Height = 6.65

FITNESS FUNCTION

Fitness = Shadow Cast + Surface Area - Volume + Height

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Computation Grasshopper Definition The first step to the computation was to understand the capabilities and limitations of the tool, in this case we are using grasshopper, python and galapagos as an evolutionary solver.

Fitness = (Shadow Cast/Volume) + Height - (|lowest point|-0) Shadow

Volume

Height

Floor Dist

Fitness

0.14

0.01

0.31

0

13.17

Genes 1

Fitness = (Shadow Cast/Volume) + 10*Height - (|lowest point|-0) Shadow

Volume

Height

Floor Dist

Fitness

35.79

45.48

7.47

4.47

71.1

Genes 1

During the manual process we were able to control all the parameters, breeding strategies, number of mutations etc. Hence we could analyze every parameter and compare each generation more thoroughly, this was not the case when using galapagos as it is very difficult to store and extract data from it.

Each population is composed of twenty individuals except the first the generations that had an initial boost with a total of a hundred individuals. This allowed us to have a very diverse first generation.

Fitness = (10*Shadow Cast/Volume) + Height - (|lowest point|-0) Shadow

Volume

Height

Floor Dist

Fitness

0.13

0.13

0.31

0

13.17

Genes 1

Fitness = (Shadow Cast/10*Volume) + Height - 20*(|lowest point|-0) Shadow

Volume

Height

Floor Dist

Fitness

40.41

32.18

5.43

0

5.4

Genes 1

Fitness = (Shadow Cast/10*Volume) + Height - (|lowest point|-0) Shadow

Volume

Height

Floor Dist

Fitness

29.6

30.7

6.8

0.09

6.8

Genes 1

Fitness = (Shadow Cast/Volume) + Height - (|lowest point|-0) Shadow

Volume

Height

Floor Dist

Fitness

1.24

0.28

0.95

0

48.2

Genes 2

Each pythonscript component describes one gene and the concatenation of several of them defines the entire genome. Each component contains three inputs. The first slider will select a gene from the gene pool and the other two sliders will pick values from a predefined list of vector, points.

When we were scripting the component we decided to make sure all the sliders had a use, since not every set of instructions require different input values, we made sure that the sliders had an effect on every set of instructions. This was a mistake that we later noticed as it introduced a random factor. This meant that galapagos couldn’t not find an optimal solution as every time the sliders moved the result would be completely different. This can be appreciated in figure. As a conclusion for future works both fitness functions and genes should be carefully thought out as small glitches might return undesirable results.

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Computation Digital Process

Fitness = (Shadow Cast/Volume) + 10*Height - (|lowest point|-0) Shadow

Volume

Height

Floor Dist

Fitness

28.2

31.06

7.6

3

73.91

Genes 2

Fitness = (Shadow Cast/Volume) + 10*Height - (|lowest point|-0) Shadow

Volume

Height

Floor Dist

Fitness

28.79

36.35

8.67

5.31

82.18

Genes 4

Fitness = (Shadow Cast/Volume) + 10*Height - (|lowest point|-0) Shadow

Volume

Height

Floor Dist

Fitness

28.11

39.49

7.55

3.74

72.47

Genes 3

Fitness = (Shadow Cast/Volume) + Height*Height - (|lowest point|-0) Shadow

Volume

Height

Floor Dist

Fitness

29.49

35.34

7.6

4.11

54.48

Genes 4

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Fitness function

Weighting the parameters

figure[x1] Fitness = Shadow Area (m2) + Surface Area (m2) + Height (m) - Height from the Ground (m) *

In figure [x2] the fitness function is measuring the fitness of the primitive, we can appreciate that the initial values have different ranges. The surface area of the primitive is a much higher than any of the other variables making it an attractive objective for the GA to increase the fitness function. If we intend to make sure all the variables have a chance of affecting the solver we should remap or weight the parameters figure [x3].

*The function penalised if the geometry gets lifted from the ground

Thinking of variables as unit less

values were not correlated to the number of genes. eg. The height parameter that penalises the function if the geometry is lifted from the ground is only affected by the first gene independently of the length of the instruction list.

figure [x3] eg.

Fitness = W1*SH + W2*SA + W3*H -HG

In a simple fitness function such as figure[x1] all the parameters are treated equally. All the the variables taken into account might have different units therefore it is very unlikely that even if the function is linear all the variables might have the same effect on the final result.

A good approach for defining a fitness function might be to consider all the variables unit less. This will make all the variables comparable and you could easily spot if all of the variables are in the same range. The sign in front of every variable will define if we want to maximize or minimize that variable in particular. It is very unlikely that all the variables taken into account have. figure [x2] eg.

Fitness = SH + SA + H -HG

Fitness = 34.8 + 87.4 + 4 - 0

Computation Fitness Function

Fitness = 1*SH + 0.5*SA + 10*H -HG Fitness = 34.8 + 43.7 + 40 - 0 In the new weighted fitness all the variables are in the same range, therefore the have the same importance in the overall fitness.

The function should be scalable One thing that we started to notice is as we increased the number of genes some of the values of the fitness function lost the opportunity to affect the result due to the fact that the other values were increasing linearly as we increased the number of genes and these other

FITNESS FUNCTION

Fitness = (W*Shadow Cast/W*Volume) + W*Height - W*(|lowest point|-0)

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

As a conclusion we think GAs can be used to create unexpected high performance designs. This wouldn’t not be achievable without the computer due to the fact that the manual process is very time consuming as we have experimented with both process. With the power of computers we were able to replicate the manual process into a more cost effective and affordable procedure.

Computation Conclusion

In order to have an efficient GA a quantifiable criteria must be determined. This has been of the key aspect of the exercise. We had tried to decompose and understand the fitness criteria and how several variables could be measured and quantified independently of their nature. It ended being a process of fine tuning and weighing the different parameters so all of the parameters measured could have the same effect in the overall result.

In some stages of the project we analysed several breeding strategies. After several generations we figured that more than a breeding strategy we were mutating entire populations. This could be appreciated in the graphs where you can see that the entire population had a greater standard deviation and that the normal distribution graph would no longer apply. The entire population got spreaded in two extreme groups.

This turnout to be a valuable lesson for analysis, this mistake proved the benefits of mutation as the overall population got fitter and we got more variation.

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Code import rhinoscriptsyntax as rs import math def Move(primitive,vector): geom = rs.MoveObject(primitive, vector) return geom def Copy(primitive,vector): geomlist = [] geomlist.append(primitive) geom = rs.CopyObject(primitive,vector) geomlist.append(geom) return geomlist def Rotate(primitive,pt,angle,axis,copy): geom = rs.RotateObject(primitive,pt,angle,axis,copy) return geom def Scale(primitive,pt,Scale_Factor,copy): geom = rs.ScaleObject(primitive,pt,Scale_Factor,copy) return geom def Mirror(primitive, start_pt, end_pt, copy): geom = rs.MirrorObject(primitive, start_pt, end_pt, copy) return geom def PolarArray(primitive,pt,angle,axis,count):

Computation Code Script

geom = [] for i in range(0,count): new_geom = rs.RotateObject(primitive,pt,angle,axis,True) geom.append(new_geom) primitive = new_geom return geom def Pt(primitive): CM = rs.SurfaceAreaCentroid(primitive)[0] surfaces = rs.ExplodePolysurfaces(primitive) crv = [] pts = [] for i in range(0,len(surfaces)): crv_extract = rs.DuplicateEdgeCurves(surfaces[i]) crv.extend(crv_extract) for i in range(0,len(crv)): domain = rs.CurveDomain(crv[i]) pt_start = rs.EvaluateCurve(crv[i],domain[0]) pt_end = rs.EvaluateCurve(crv[i],domain[1]) pts.append(pt_start) pts.append(pt_end) new_pts = rs.CullDuplicatePoints(pts) new_pts.append(CM) return new_pts #Instruction List

instructions = [] instructions.append(Mirror) instructions.append(Rotate) instructions.append(Scale) instructions.append(PolarArray) instructions.append(Move) instructions.append(Copy) #Vector List vector = Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Computation Fitness Code Script

Code for the Fitness Function surface_area = rs.SurfaceArea(geom) surface_volume = rs.SurfaceVolume(geom) print "Surface Area: ", surface_area[0] print "Volume: ", surface_volume[0] print "Shadow Area: ", shadow_area[0] bound_box_pts = rs.BoundingBox(geom) pt_length = len(bound_box_pts) #Get the height of the object #Height is good z_high_value = 0 for i in range(0,pt_length): pt = bound_box_pts[i] z_value = pt[2]#get the z value if z_value > z_high_value: z_high_value = z_value #get the lowest point of the bbox // so the geom does not lift from the floor z_low_value = 100 for i in range(0,pt_length): pt = bound_box_pts[i] z_value = pt[2]#get the z value if z_value < z_low_value: z_low_value = z_value z_total = z_high_value - z_low_value print "Height: ", z_total

fitness = SH*shadow_area[0]*shadow_area[0] + SA*surface_area[0]*surface_area[0] + Height*z_ total*z_total - Volume*surface_volume[0]*surface_volume[0] a = fitness Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Bibliography “The Architecture of Emergence”, The Evolution of Form in Nature and Civilisation, Michael Weinstock, John Wiley & Sons.

“Endless Forms Most Beautiful”, The New Science of Evo Devo and the Making of the Animal Kingdom, Sean Carrol, Quercus.

“Evolution and computation”, Emergence Technologies and Design, Towards a biological paradigm for architecture. Michael Hensel, Achim Menges and Michael Weinstock.

“From Control to Design”, Parametric/ Algorithmic architecture, Michael Meredith, AGU, ARUP, Mutusuro Sasaki, Adams Kara Taylor and Aranda/ Lasch.

“Information is beautiful”, David Mc Candless “Information Graphics”, Sandra Rendgen “Data Structures and Algorithms”, Granville Barnett and Luca Del Tongo, Data Structures and Algorithms “Emergence in Architecture”, AD n. 74 (May-June 2004), Michael Hensel, Achim Menges “Emergent Structural Morphology”, AD n. 72 (Jan 2002 2002), Peter Testa, Devyn Weiser “The Diagrams of Architecture”, AD Reader, Mark Garcia “Scripting Cultures” Architectural Design and Programming, AD Primer, Mark Burry “Computational Design Thinking”, AD Reader, Achim Menges, Sean Ahlquist

“Digital Workflows in Architecture”, Design Assembly Industry, Scott Marble David Rutten, http://ieatbugsforbreakfast.wordpress.com/2011/03/07/define-fitness/ Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013

Emergent Technologies and Design EMERGENCE SEMINAR Group 03 | 2012-2013