Radial basis function network configuration using genetic algorithms

In the present study, genetic algorithms are proposed to automatically configure RBF networks and the network configuration is formed as a subset selection problem to find an optimal subset of nc terms from the Nt training data samples.

Abstract

Most training algorithms for radial basis function (RBF) neural networks start with a predetermined network structure which is chosen either by using a priori knowledge or based on previous experience. The resulting network is often insufficient or unnecessarily complicated and an appropriate network structure can only be obtained by trial and error. Training algorithms which incorporate structure selection mechanisms are usually based on local search methods and often suffer from a high probability of being trapped at a structural local minima. In the present study, genetic algorithms are proposed to automatically configure RBF networks. The network configuration is formed as a subset selection problem. The task is then to find an optimal subset of nc terms from the Nt training data samples. Each network is coded as a variable length string with distinct integers and genetic operators are proposed to evolve a population of individuals. Criteria including single objective and multiobjective functions are proposed to evaluate the fitness of individual networks. Training based on a practical data set is used to demonstrate the performance of the new algorithms.