Cap6938 neuroevolution and artificial embryogeny evolutionary comptation
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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


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


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


  • 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 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 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


  • 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


  • 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


Mitchell pp. 117-38, and ch.5 (pp. 170-177)

No Free Lunch Theorems for Optimization

by Wolpert and Macready (1996)