A Symbiotic CHC Co-evolutionary Algorithm for Automatic RBF Neural Networks Design

  • Elisabet Parras-Gutierrez
  • Ma José del Jesus
  • Juan J. Merelo
  • Víctor M. Rivas
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 50)


This paper introduces Symbiotic_CHC_RBF, a co-evolutionary algorithm intended to automatically establish the parameters needed to design models for classification problems. Co-evolution involves two populations, which evolve together by means of a symbiotic relationship. One of the populations is the method EvRBF, which provides the design of radial basis function neural nets by means of evolutionary algorithms. The second population evolves sets of parameters for the method EvRBF, being every individual of the population a configuration of parameters for the method. Results show that Symbiotic_CHC_RBF can be effectively used to obtain good models, while reducing significantly the number of parameters to be fixed by hand.


neural networks evolutionary algorithms co-evolution parameter estimation CHC algorithm 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Elisabet Parras-Gutierrez
    • 1
  • Ma José del Jesus
    • 1
  • Juan J. Merelo
    • 2
  • Víctor M. Rivas
    • 1
  1. 1.Department of Computer SciencesJaénSpain
  2. 2.Department of Computers Architecture and TechnologyGranadaSpain

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