Feedforward neural networks configuration using evolutionary programming

This paper proposes an evolutionary programming based neural network construction algorithm, that efficiently configures feedforward neural networks in terms of optimum structure and optimum parameter set. The proposed method determines the appropriate structure, i.e. an appropriate number of hidden nodes, in such a way that locally optimal solutions are avoided. While choosing the number of hidden nodes, this method performs a trade-off between generalization and memorization. In this method, the network is evolved so that it learns an optimum parameter set, i.e. weights and bias, without being trapped into a locally optimal solution. Efficiency of this method is further enhanced by incorporating the concepts of adaptive structural mutation. Finally, efficacy of the proposed scheme is demonstrated on a Contract Bridge game opening bid problem