Scalable Continuous Multiobjective Optimization with a Neural Network–Based Estimation of Distribution Algorithm

  • Luis Martí
  • Jesús García
  • Antonio Berlanga
  • José M. Molina
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4974)


To achieve a substantial improvement of MOEDAs regarding MOEAs it is necessary to adapt their model building algorithm to suit this particular task. Most current model building schemes used so far off–the–shelf machine learning methods. However, the model building problem has specific requirements that those methods do not meet and even avoid.

In this we work propose a novel approach to model building in MOEDAs using an algorithm custom–made for the task. We base our proposal on the growing neural gas (GNG) network. The resulting model–building GNG (MB–GNG) is capable of yielding good results when confronted to high–dimensional problems.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Luis Martí
    • 1
  • Jesús García
    • 1
  • Antonio Berlanga
    • 1
  • José M. Molina
    • 1
  1. 1.Universidad Carlos III de Madrid, Group of Applied Artificial IntelligenceAv. de la Universidad Carlos IIIMadridSpain

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