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

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.