Evolving Weights and Transfer Functions in Feed Forward Neural Networks

In this paper we show different evolutionary algorithms applied to the simultaneous off-line evolution of weights and transfer functions of feed-forward neural networks. Experimentation has been carried out with classical benchmarks when weights and both weights and transfer function are evolved and a comparison of the proposed evolutionary methods with classical methodologies (the back-propagation algorithm) are shown. Results are very promising and show the effectiveness of the addressed evolutionary methodologies to solve the problem of simultaneously finding the optimal weights and transfer functions of a neural network.