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On the Computational Properties of the Multi-Objective Neural Estimation of Distribution Algorithm

  • Luis Martí
  • Jesús García
  • Antonio Berlanga
  • José M. Molina
Part of the Studies in Computational Intelligence book series (SCI, volume 236)

Abstract

This paper explores the behavior of the multi–objective neural EDA (MONEDA) in terms of its computational requirements it demands and assesses how it scales when dealing with multi–objective optimization problems with relatively large amounts of objectives. In order to properly comprehend these matters other MOEDAs and MOEAs are included in the analysis. The experiments performed tested the ability of each approach to scalably solve many–objective optimization problems. The fundamental result obtained is that MONEDA is not only yields similar or better solutions when compared with other approaches but also does it with at a lower computational cost.

Keywords

Multi–objective Optimization Computational Complexity Multi–objective Optimization Evolutionary Algorithms Estimation of Distribution Algorithms (EDAs) 

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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.Group of Applied Artificial IntelligenceUniversidad Carlos III de Madrid.MadridSpain

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