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Evolutionary Bayesian Classifier-Based Optimization in Continuous Domains

  • Teresa Miquélez
  • Endika Bengoetxea
  • Pedro Larrañaga
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4247)

Abstract

In this work, we present a generalisation to continuous domains of an optimization method based on evolutionary computation that applies Bayesian classifiers in the learning process. The main difference between other estimation of distribution algorithms (EDAs) and this new method –known as Evolutionary Bayesian Classifier-based Optimization Algorithms (EBCOAs)– is the way the fitness function is taken into account, as a new variable, to generate the probabilistic graphical model that will be applied for sampling the next population.

We also present experimental results to compare performance of this new method with other methods of the evolutionary computation field like evolution strategies, and EDAs. Results obtained show that this new approach can at least obtain similar performance as these other paradigms.

Keywords

Evolutionary Computation Continuous Domain Distribution Algorithm Probabilistic Graphical Model Evolution Strategy 
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 2006

Authors and Affiliations

  • Teresa Miquélez
    • 1
  • Endika Bengoetxea
    • 1
  • Pedro Larrañaga
    • 1
  1. 1.Intelligent Systems GroupUniversity of the Basque Country, Computer Engineering FacultySan SebastianSpain

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