Advancing Model–Building for Many–Objective Optimization 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 6024)


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.


Distribution Algorithm Adaptive Resonance Theory Covariance Matrix Adaptation Building Algorithm Base Learning 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 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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