An improved radial basis function neural network based on a cooperative coevolutionary algorithm for handwritten digits recognition

Abstract

Co-evolutionary algorithms are a class of adaptive search meta-heuristics inspired from the mechanism of reciprocal benefits between species in nature. The present work proposes a cooperative co-evolutionary algorithm to improve the performance of a radial basis function neural network (RBFNN) when it is applied to recognition of handwritten Arabic digits. This work is in fact a combination of ten RBFNNs where each of them is considered as an expert classifier in distinguishing one digit from the others; each RBFNN classifier adapts its input features and its structure including the number of centres and their positions based on a symbiotic approach. The set of characteristic features and RBF centres have been considered as dissimilar species where each of them can benefit from the other, imitating in a simplified way the symbiotic interaction of species in nature. Co-evolution is founded on saving the best weights and centres that give the maximum improvement on the sum of squared error of each RBFNN after a number of learning iterations. The results quality has been estimated and compared to other experiments. Results on extracted handwritten digits from the MNIST database show that the co-evolutionary approach is the best.