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CAP6938 Neuroevolution and Artificial Embryogeny Evolutionary Comptation

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

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  1. CAP6938Neuroevolution and Artificial EmbryogenyEvolutionary Comptation Dr. Kenneth Stanley January 23, 2006

  2. 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

  3. Survival of the Roundest Gen 1 Select as parents Gen 2 Select as parents Gen 3 Champ!

  4. 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

  5. Major Concepts • Genotype and Phenotype • Representation / mapping • Evaluation and fitness • Generations • Steady state • Selection • Mutation • Mating/Crossover/Recombination • Premature Convergence • Speciation

  6. 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

  7. 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

  8. Mappings

  9. 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

  10. 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…

  11. 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

  12. 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)

  13. 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

  14. 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?

  15. 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

  16. 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

  17. 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

  18. 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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