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ga1 - Geneticalgorithms Introduction...

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Genetic algorithms Introduction [email protected]
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Genetic Algorithms in a slide Premise Evolution worked once (it produced us!), it might work again Basics Pool of solutions Mate existing solutions to produce new solutions Mutate current solutions for long-term diversity Cull population  
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Originator John Holland Seminal work Adaptation in Natural and Artificial Systems introduced main GA  concepts, 1975
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Introduction Computing pioneers (especially in AI) looked to natural systems as  guiding metaphors Evolutionary computation Any biologically-motivated computing activity simulating natural  evolution Genetic Algorithms are one form of this activity Original goals Formal study of the phenomenon of adaptation John Holland An optimization tool for engineering problems
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Main idea Take a population of candidate solutions to a given problem Use operators inspired by the mechanisms of natural genetic variation Apply selective pressure toward certain properties Evolve a more fit solution 
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Why evolution as a metaphor Ability to efficiently guide a search through a large solution space Ability to adapt solutions to changing environments “Emergent” behavior is the goal “The hoped-for emergent behavior is the design of high-quality  solutions to difficult problems and the ability to adapt these  solutions in the face of a changing environment”  Melanie Mitchell, An Introduction to Genetic Algorithms
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Evolutionary terminology Abstractions imported from biology Chromosomes, Genes, Alleles Fitness, Selection Crossover, Mutation
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GA terminology In the spirit – but not the letter – of  biology GA chromosomes are strings of genes Each gene has a number of alleles; i.e., settings Each chromosome is an encoding of a solution to a problem A population of such chromosomes is operated on by a GA
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Components of a GA A problem to solve, and . .. Encoding technique        ( gene, chromosome ) Initialization procedure                 (creation) Evaluation function                  (environment) Selection of parents                (reproduction) Genetic operators     (mutation, recombination) Parameter settings              (practice and art)
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Simple Genetic Algorithm { initialize population; evaluate population; while TerminationCriteriaNotSatisfied { select parents for reproduction; perform recombination and mutation; evaluate population; } }
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The GA Cycle of Reproduction reproduction population evaluation modification discard deleted members parents children modified children evaluated children
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Population
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