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Designing a Phenotypic Distance Index for Radial Basis Function Neural Networks

  • Jesús González
  • Ignacio Rojas
  • Héctor Pomares
  • Julio Ortega
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2686)

Abstract

MultiObjective Evolutionary Algorithms (MOEAs) may cause a premature convergence if the selective pressure is too large, so, MOEAs usually incorporate a niche-formation procedure to distribute the population over the optimal solutions and let the population evolve until the Pareto-optimal region is completely explored. This niche-formation scheme is based on a distance index that measures the similarity between two solutions in order to decide if both may share the same niche or not. The similarity criterion is usually based on a Euclidean norm (given that the two solutions are represented with a vector), nevertheless, as this paper will explain, this kind of metric is not adequate for RBFNNs, thus being necessary a more suitable distance index. The experimental results obtained show that a MOEA including the proposed distance index is able to explore sufficiently the Pareto-optimal region and provide the user a wide variety of Pareto-optimal solutions.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Jesús González
    • 1
  • Ignacio Rojas
    • 1
  • Héctor Pomares
    • 1
  • Julio Ortega
    • 1
  1. 1.Department of Computer Architecture and Computer TechnologyUniversity of GranadaGranada

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