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Genetic Algorithms

  • James E. Smith
Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 62)

Abstract

Evolutionary Algorithms are search algorithms based on the Darwinian metaphor of “Natural Selection”. Typically these algorithms maintain a finite memory, or “population” of individual solutions (points on the search landscape), each of which has a scalar value, or “fitness” attached to it, which in some way reflects the quality of the solution. The search proceeds via the iterative generation, evaluation and possible incorporation of new individuals based on the current population. A number of classes of Evolutionary Algorithms can (broadly speaking) be distinguished by the nature of the alphabet used to represent the search space and the specialisation of the various operators used, e.g. Genetic Algorithms (Holland, 1975: binary or finite discrete representations), Evolution Strategies (Rechenberg, 1973; Schwefel, 1981: real numbers), Evolutionary Programming (Fogel et al., 1966: real numbers), and Genetic Programming (Cramer, 1985; Koza, 1989: tree based representation of computer programs). Although not originally designed for function optimisation, genetic algorithms have been shown to demonstrate an impressive ability to locate optima in large, complex and noisy search spaces.

Keywords

Genetic Algorithm Mutation Rate Uniform Crossover Adaptive Genetic Algorithm Recombination Operator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 2002

Authors and Affiliations

  • James E. Smith
    • 1
  1. 1.Intelligent Computer Systems Centre Faculty of Computer Studies and MathematicsUniversity of the West of EnglandBristolUK

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