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Critical Issues in Model-Based Surrogate Functions in Estimation of Distribution Algorithms

  • Roberto Santana
  • Alexander Mendiburu
  • Jose A. Lozano
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8298)

Abstract

In many optimization domains the solution of the problem can be made more efficient by the construction of a surrogate fitness model. Estimation of distribution algorithms (EDAs) are a class of evolutionary algorithms particularly suitable for the conception of model-based surrogate techniques. Since EDAs generate probabilistic models, it is natural to use these models as surrogates. However, there exist many types of models and methods to learn them. The issues involved in the conception of model-based surrogates for EDAs are various and some of them have received scarce attention in the literature. In this position paper, we propose a unified view for model-based surrogates in EDAs and identify a number of critical issues that should be dealt with in order to advance the research in this area.

Keywords

estimation of distribution algorithms surrogate functions function approximation probabilistic modeling most probable configuration abductive inference 

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Roberto Santana
    • 1
  • Alexander Mendiburu
    • 1
  • Jose A. Lozano
    • 1
  1. 1.Intelligent Systems Group, Department of Computer Science and Artificial IntelligenceUniversity of the Basque Country (UPV/EHU)San SebastianSpain

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