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Unformatted text preview: Genetic algorithms Introduction [email protected] 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 long­term diversity Cull population Originator John Holland Seminal work Adaptation in Natural and Artificial Systems introduced main GA concepts, 1975 Introduction Computing pioneers (especially in AI) looked to natural systems as guiding metaphors Evolutionary computation Any biologically­motivated 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 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 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 hoped­for emergent behavior is the design of high­quality 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 Evolutionary terminology Abstractions imported from biology Chromo...
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This note was uploaded on 04/05/2010 for the course CS 723 taught by Professor Sc during the Spring '10 term at Jaypee University IT.

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