Evolutionary optimization of RBF networks

Abstract

One of the main obstacles to the widespread use of artificial neural networks is the difficulty of adequately defining values for their free parameters. The article discusses how radial basis function (RBF) networks can have their parameters defined by genetic algorithms. For such, it presents an overall view of the problems involved and the different approaches used to genetically optimize RBF networks. Finally, a model is proposed which includes representation, crossover operator and multiobjective optimization criteria. Experimental results using this model are presented.