CAP6938
Neuroevolution and
Artificial Embryogeny
Evolutionary Comptation
Dr. Kenneth Stanley
January 23, 2006
Main Idea
• Natural selection can work on computers
– Selection: Picking the best parents
– Variation: Mutation and Mating
•
•
•
•
Start with some really bad individuals
Some are always better than others
Survival of the fittest leads to improvement
Progress occurs over generations
Survival of the Roundest
Gen 1
Select as parents
Gen 2
Select as parents
Gen 3
Champ!
Several Versions of EC
•
•
•
•
•
Genetic Algorithms (Holland 1960s)
Evolution Strategies (Rechenberg 1965)
Evolution Programming (Fogel 1966)
Genetic Programming? (Smith 1980,Koza 1982)
The process is more important than the name
Major Concepts
•
•
•
•
•
•
•
•
•
•
Genotype and Phenotype
Representation / mapping
Evaluation and fitness
Generations
Steady state
Selection
Mutation
Mating/Crossover/Recombination
Premature Convergence
Speciation
Genotype and Phenotype
• Genotype means the code (e.g. DNA) used to
the describe an organism, i.e. the “blueprint”
10010110110
• Phenotype is the organism’s actual realization
f ( x)  3x 2  7 x  10
Representation and Mapping
• The genotype is a representation of the
phenotype; how to represent information is
a profound and deep issue
• The process of creating the phenotype
from the genotype is called the genotype
to phenotype mapping
• Mapping can happen in many ways
Mappings
Evaluation and Fitness
• The phenotype is evaluated, not the
genotype
• The performance of the phenotype during
evaluation is its fitness
• Fitness tells us which genotypes are better
than others
Generations
• Most GAs proceed generationally:
– A whole population is evaluated one at a time
– That is the current generation
– They then are replaced en masse by their
offspring
– The replacements form the next generation
– And so on…
Steady State Evolution
• Not all EC is generational
• It is possible to replace only one individual
at a time, i.e. steady state evolution
• Common in Evolution Strategies (ES)
• Also called real-time or online evolution
• Another twist: Phenotypes can be
evaluated simultaneously and
asynchronously
Selection
• Selection means deciding who should be a
parent and who should not
• Selection is usually based on fitness
• Methods of selection (see Mitchell p.166)
– Roulette Wheel (probability based on fitness)
– Truncation (random among top n%)
– Rank selection (use rank instead of fitness)
– Elitism (champs get to have clones)
Mutation
• Mutation means changing the genotype
randomly
• Can vary from strong (every gene
mutates) to weak (only one gene mutates)
• May mean adding a new gene entirely
• Mutation prevents fixation
• Mutation is a source of diversity and
discovery
Mating
• Combining one or more genomes
• Many ways to implement crossover:
– Singlepoint
– Multipoint (Uniform)
– Multipoint average (Linear)
• How important is crossover?
• What is it for?
Premature Convergence
• When a single genotype dominates the
population, it is converged
• Convergence is premature if a suitable
solution has not yet been found
• Premature convergence is a significant
concern in EC
• Hence the need to maintain diversity
Speciation
•
•
•
•
•
A population can be divided into species
Can prevents incompatibles from mating
Can protects innovative concepts in niches
Maintains diversity
Many methods
– Islands
– Fitness sharing
– Crowding
Natural Evolution is not Just
Optimization
•
•
•
•
•
•
What is the optimum?
What is the space being searched?
What are the dimensions?
Herb Simon (1958): “Satisficing”
Is evolution even just a satisficer?
Evolution satisfices and complexifies
Next Class:
Theoretical Issues in EC
• The Schema Theorem
• No Free Lunch
Homework:
Mitchell pp. 117-38, and ch.5 (pp. 170-177)
No Free Lunch Theorems for Optimization
by Wolpert and Macready (1996)