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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

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cap6938 neuroevolution and artificial embryogeny evolutionary comptation

CAP6938Neuroevolution and Artificial EmbryogenyEvolutionary Comptation

Dr. Kenneth Stanley

January 23, 2006

main idea
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
Survival of the Roundest

Gen 1

Select as parents

Gen 2

Select as parents

Gen 3

Champ!

several versions of ec
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
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 and Phenotype
  • Genotype means the code (e.g. DNA) used to the describe an organism, i.e. the “blueprint”
  • Phenotype is the organism’s actual realization

10010110110

representation and mapping
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
evaluation and fitness
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
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
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
  • 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
  • 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
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
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
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
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
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)

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