CAP6938 Neuroevolution and Developmental Encoding Evolutionary Comptation

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CAP6938Neuroevolution and Developmental
EncodingEvolutionary Comptation

• Dr. Kenneth Stanley
• September 11, 2006

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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

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

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Major Concepts

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

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Genotype and Phenotype

• Genotype means the code (e.g. DNA) used to the
  describe an organism, i.e. the blueprint
• Phenotype is the organisms actual realization

10010110110
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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

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Mappings
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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

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Generations

• Most GAs proceed in generations
• 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

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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

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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)

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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

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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?

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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

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Speciation

• A population can be divided into species
• Can prevent incompatibles from mating
• Can protect innovative concepts in niches
• Maintains diversity
• Many methods
• Islands
• Fitness sharing
• Crowding

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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

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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)