Advancing Model–Building for Many–Objective Optimization Estimation of Distribution Algorithms

  • Luis Martí
  • Jesús García
  • Antonio Berlanga
  • José M. Molina
Conference paper

DOI: 10.1007/978-3-642-12239-2_53

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6024)
Cite this paper as:
Martí L., García J., Berlanga A., Molina J.M. (2010) Advancing Model–Building for Many–Objective Optimization Estimation of Distribution Algorithms. In: Di Chio C. et al. (eds) Applications of Evolutionary Computation. EvoApplications 2010. Lecture Notes in Computer Science, vol 6024. Springer, Berlin, Heidelberg

Abstract

In order to achieve a substantial improvement of MOEDAs regarding MOEAs it is necessary to adapt their model–building algorithms. Most current model–building schemes used so far off–the–shelf machine learning methods. These methods are mostly error–based learning algorithms. However, the model–building problem has specific requirements that those methods do not meet and even avoid.

In this work we dissect this issue and propose a set of algorithms that can be used to bridge the gap of MOEDA application. A set of experiments are carried out in order to sustain our assertions.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Luis Martí
    • 1
  • Jesús García
    • 1
  • Antonio Berlanga
    • 1
  • José M. Molina
    • 1
  1. 1.Group of Applied Artificial IntelligenceUniversidad Carlos III de MadridMadridSpain

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