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Optimal Mutation Rate Using Bayesian Priors for Estimation of Distribution Algorithms

  • Thilo Mahnig
  • Heinz Mühlenbein
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2264)

Abstract

UMDA(the univariate marginal distribution algorithm) was derived by analyzing the mathematical principles behind recombination. Mutation, however, was not considered. The same is true for the FDA (factorized distribution algorithm), an extension of the UMDA which can cover dependencies between variables. In this paper mutation is introduced into these algorithms by a technique called Bayesian prior. We derive theoretically an estimate how to choose the Bayesian prior. The recommended Bayesian prior turns out to be a good choice in a number of experiments. These experiments also indicate that mutation increases in many cases the performance of the algorithms and decreases the dependence on a good choice of the population size.

Keywords

Bayesian prior univariate marginal distribution algorithm mutation estimation of distribution algorithm 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Thilo Mahnig
    • 1
  • Heinz Mühlenbein
    • 1
  1. 1.RWCP Theoretical Foundation GMD LaboratorySankt AugustinGermany

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