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Adaptive crossover using automata

  • Tony White
  • Franz Oppacher
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 866)

Abstract

Genetic Algorithms (GAs) have traditionally required the specification of a number of parameters that control the evolutionary process. In the classical model, the mutation and crossover operator probabilities are specified before the start of a GA run and remain unchanged; a so-called static model. This paper extends the conventional representation by using automata in order to allow the adaptation of the crossover operator probability as the run progresses in order to facilitate schema identification and reduce schema disruption. Favourable results have been achieved for a wide range of function minimization problems and these are described.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • Tony White
    • 1
  • Franz Oppacher
    • 2
  1. 1.Bell-Northern ResearchOttawaCanada
  2. 2.School of Computer ScienceCarleton UniversityOttawaCanada

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