Evolving both size and accuracy of RBF networks using Memetic Algorithm

One of the main obstacles to obtain an artificial neural network with reasonable performance is the parameter setting. This work proposes a methodology to the automatic definition of RBF (radial basis function) networks with an appropriate configuration for the selected classification problems. We propose the use of a memetic algorithm in order to perform the search for networks with minimum architecture and error rate. A set of experiments was made with four datasets and we were able to show the effectiveness of the method.