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On the Model–Building Issue of Multi–Objective Estimation of Distribution Algorithms

  • Luis Martí
  • Jesús García
  • Antonio Berlanga
  • José M. Molina
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5572)

Abstract

It has been claimed that perhaps a paradigm shift is necessary in order to be able to deal with this scalability issue of multi–objective optimization evolutionary algorithms. Estimation of distribution algorithms are viable candidates for such task because of their adaptation and learning abilities and simplified algorithmics. Nevertheless, the extension of EDAs to the multi–objective domain have not provided a significant improvement over MOEAs.

In this paper we analyze the possible causes of this underachievement and propose a set of measures that should be taken in order to overcome the current situation.

Keywords

Evolutionary Computation Multiobjective Optimization Objective Optimization Objective Space Distribution Algorithm 
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 2009

Authors and Affiliations

  • Luis Martí
    • 1
  • Jesús García
    • 1
  • Antonio Berlanga
    • 1
  • José M. Molina
    • 1
  1. 1.GIAA, Dept. of InformaticsUniversidad Carlos III de MadridMadridSpain

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