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An individually variable mutation-rate strategy for genetic algorithms

  • Stephen A. Stanhope
  • Jason M. Daida
Enhanced Evolutionary Operators
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1213)

Abstract

In Neo-Darwinism, mutation can be considered to be unaffected by selection pressure. This is the metaphor generally used by the genetic algorithm for its treatment of the mutation operation, which is usually regarded as a background operator. This metaphor, however, does not take into account the fact that mutation has been shown to be affected by external events. In this paper, we propose a mutation-rate strategy that is variable between individuals within a given generation based on the individual's relative performance for the purpose of function optimization.

Keywords

Genetic Algorithm Mutation Rate Relative Fitness Fitness Landscape Mutation Operation 
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-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Stephen A. Stanhope
    • 1
  • Jason M. Daida
    • 2
  1. 1.Environmental Research Institute of MichiganAdvanced Information Systems GroupAnn ArborUSA
  2. 2.Artificial Intelligence Laboratory & Space Physics Research LaboratoryThe University of MichiganAnn ArborUSA

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