Genetic Algorithms. 2 Introduction To Genetic Algorithms (GAs)
John Gero Lecture Genetic Algorithms
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MIT Design Inquiry How do genetic algorithms relate to their biological origins? How do they relate to human processes of design? Why is there power in this metaphor? How can they be used to extend the capabilities of the designer? John Gero Professor of Design Science University of Sydney Visiting Professor of Design and Computation MIT
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John Gero
Transcript of John Gero Lecture Genetic Algorithms
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MIT Design Inquiry
How do genetic algorithms relate to theirbiological origins?
How do they relate to human processes ofdesign?
Why is there power in this metaphor?
How can they be used to extend thecapabilities of the designer?
John GeroProfessor of Design Science
University of SydneyVisiting Professor of Design and Computation
MIT
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MIT Design Inquiry
How do genetic algorithms relate to theirbiological origins?
l Separation of genetic material (genotype[representation]) from organism (phenotype[design])
l Expressing genotype as organisml Organism carries genotype and reproduces
genotype using genetic processes of crossoverand mutation
l Darwins natural selection uses fitnesses oforganisms in their environment to improve the genepool
l GA is a simple model of this process
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Genetic processesGenetic processes
Crossover Points
A
C
B
D
Parents
Offspring
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A A B
B B C
A B C
B A B
Parents
Crossover point
Offspring
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A A B
B B C
A A C
B B B
Parents Offspring
Crossover point
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How do they relate to human processes ofdesign?
l Can map genetic representation onto acomputational representation of a design; can mapphenotype onto a interpretable view of a design
l Humans work on single or few designs at a time/genetics works on a population of designs in parallel
l Humans can be seen to search design spaces - thisis one interpretation of what GAs are doing.
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Why is there power in this metaphor?
l Guaranteed improvement - Darwinianevolution
l Large scale searchl Blind searchl Fitness can be human evaluationl Fitness can change over evolutionary timel Can produce complexityl Can produce unexpected results
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How can they be used to extend the capabilitiesof the designer?
l Creativity through genetic engineering
l Novel designs through extending geneticcrossover
l Novel designs through different fitnesses
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Genetic Engineering and CreativeDesign
Genetic Engineering and CreativeDesign
l Background
l genes, genotype, phenotype, fitness
l Connecting genes to performance in fitness
l Emergent gene clusters evolved genes
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xx
xx
xx
bad
good
good genotypes bad genotypes
Total Population
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a a brule 1 a ab
rule 2 aa
brule 3
a abrule 4 a rule 5 a rule 6
a ba
rule 7 ab
arule 8
ba
b
a
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design 1 design 2 design 3
design 4 design 5 design 6
design 7 design 8 design 9 design 10
{1,12,2,8,5,4,4,2,8,5,7}good
{1,2,1,8,2,8,5,5,6,6,8,1}good
{3,2,2,6,5,8,2,1,4,4,3,1}bad
{6,4,1,2,8,5,4,2,8,5,3,3}good
{3,4,8,2,8,1,6,5,7,3}bad
{2,3,2,3,4,3,5,6,5,1,6,2}neutral
{3,1,8,5,5,6,4,6,1,1,3,3}good
{1,6,4,2,7,3,4,8,6,1,6,2}bad
{6,4,1,2,3,4,5,2,1,7,4}neutral
{2,3,7,5,1,2,8,3,1,6,2,1}bad
Composite building block A{2,8,5}
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Mondrian
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Genotype Form
l In form of a tree
l Each node has four variables
l direction of rectangular split (4 values)
l fraction of the split (15 values)
l colour of split area (10 values)
l line width (3 values)
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Fitnesses for Representation
l offset between actual and required positions ofdissection lines
l number of lines with correct line width,normalised
l number of correct colour panels, normalised
l number of lines assigned, normalised
l number of unassigned lines, normalised
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Genetically Engineered Mondrian
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Genetically Engineered Mondrian
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Genetically Engineered Frank Lloyd Wright Windows
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Flondrians
l Mondrian painting genetically engineeredgenes: M-genes
l Frank Lloyd Wright windows geneticallyengineered genes in same representation: F-genes
l Flondrians are the genetic product of matingM-genes with F-genes
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How Many Designs Are There andWhere Are They?
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Genetic crossover as an interpolation
g1
g2
gcpc
p2
p1
Genotypic space GPhenotypic space P
C(g1,g2) gcC(p1,p2) pc
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P+
P
p1 p2
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Interpolation
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(interactive Genetic Art III)
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Image detection
(a) (b)
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Modelling Interest
Berlynes model of arousal based on novelty using Wundt curve
HE
DO
NIC
VA
LU
E
NOVELTYNx
Hx
Reward
Punish
0
1
-1
n1 n2
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Different novelty functions
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Different novelty preferences
N=0 N=1 N=2 N=3
N=4 N=5 N=6 N=7
N=8 N=9 N=10 N=11
N=12 N=13 N=14 N=15
N=16 N=17 N=18 N=19
