An evolutionary clustering technique with local search to design RBF neural network classifiers

Radial basis function neural networks constitute one type of feedforward neural net that requires a suitable determination of the basis functions so as to work properly. Among the many approaches available in the literature, the one proposed here combines a clustering genetic algorithm with K-means to automatically select the number and location of basis functions to be used in the RBF network. Preliminary simulation results suggest that the proposed hybrid algorithm can be successfully applied to classification problems, leading to parsimonious solutions, with competitive classification rates, when compared with other approaches from the RBF literature.