Abstract

This paper proposes a new method to design, through simulated evolution, biologically inspired receptive fields in feed forward neural networks (NNs). The method is intended to enhance pattern recognition performance by creating new neural architectures specifically tuned for a particular pattern recognition problem. It is proposed a combined neural architecture composed of two networks in cascade: a feature extraction network (FEN) followed by a neural classifier. The FEN is composed of several layers with receptive fields constructed by an additive superposition of excitatory and inhibitory fields. A genetic algorithm (GA) is used to select the receptive fields parameters to improve the classification performance. The parameters are the receptive field size, orientation, and bias as well as the number of different receptive fields in each layer. Based on a random initial population where each individual represents a different neural architecture, the GA creates new enhanced individuals. The method is applied to the problems of handwritten digit classification and face recognition. In both problems, results show strong dependency between the NN classification performance and the receptive fields architecture. The GA selected parameters of the receptive fields that produced improvements in the classification performance on the test set up to 90.8% for the problem of handwritten digit classification and up to 84.2% for the face recognition problem. On the same test sets, results were compared advantageously to standard feed forward multilayer perceptron (MLP) NNs where receptive fields are not explicitly defined. The MLP reached a maximum classification performance of 84.9% and 77.5% in both problems, respectively.