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Parallelizing the Design of Radial Basis Function Neural Networks by Means of Evolutionary Meta-algorithms

  • M. G. Arenas
  • E. Parras-Gutiérrez
  • V. M. Rivas
  • P. A. Castillo
  • M. J. Del Jesus
  • J. J. Merelo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5517)

Abstract

This work introduces SymbPar, a parallel meta-evolutionary algorithm designed to build Radial Basis Function Networks minimizing the number of parameters needed to be set by hand. Parallelization is implemented using independent agents to evaluate every individual. Experiments over classifications problems show that the new method drastically reduces the time took by sequential algorithms, while maintaining the generalization capabilities and sizes of the nets it builds.

Keywords

Neural networks evolutionary algorithms parallelization meta-evolution 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • M. G. Arenas
    • 2
  • E. Parras-Gutiérrez
    • 1
  • V. M. Rivas
    • 1
  • P. A. Castillo
    • 2
  • M. J. Del Jesus
    • 1
  • J. J. Merelo
    • 2
  1. 1.Department of Computer SciencesJaenSpain
  2. 2.Department of Computer Architecture and TechnologyCITIC-UGRGranadaSpain

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