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Customized Selection in Estimation of Distribution Algorithms

  • Roberto Santana
  • Alexander Mendiburu
  • Jose A. Lozano
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8886)

Abstract

Selection plays an important role in estimation of distribution algorithms. It determines the solutions that will be modeled to represent the promising areas of the search space. There is a strong relationship between the strength of selection and the type and number of dependencies that are captured by the models. In this paper we propose to use different selection probabilities to learn the structural and parametric components of the probabilistic graphical models. Customized selection is introduced as a way to enhance the effect of model learning in the exploratory and exploitative aspects of the search. We use a benchmark of over 15,000 instances of a simplified protein model to illustrate the gains in using customized selection.

Keywords

selection estimation of distribution algorithms optimization customized selection 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Roberto Santana
    • 1
  • Alexander Mendiburu
    • 1
  • Jose A. Lozano
    • 1
  1. 1.Department of Computer Science and Artificial IntelligenceUniversity of the Basque Country (UPV/EHU)GuipuzcoaSpain

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