Parameter Estimation for Radial Basis Function Neural Network Design by Means of Two Symbiotic Algorithms

Abstract

Increasing the usability of traditional methods is one of the key issues on future trends in data mining. Nevertheless, most data mining algorithms need to be given a suitable set of parameters for every problem they face, thus methods to automatically search the values of these parameters are required. This paper introduces two co-evolutionary algorithms intended to automatically establish the parameters needed to design radial basis function neural networks. Results show that both algorithms can be effectively used to obtain good models, while reducing significantly the number of parameters to be fixed at hand.