Evolutionary single hidden-layer feed forward networks. (23rd December 2014) - Safi, Youssef; Bouroumi, Abdelaziz
We propose an evolutionary method for optimising both the architecture and the synaptic weights of single hidden-layer feed forward neural networks. Based on evolutionary strategies, this method uses new genetic operators of mutation and recombination in order to evolve a population of candidate solutions in the form of neural networks with different architectures. Experimental results are presented to demonstrate the effectiveness of the proposed method in both classification and prediction problems. These results concern six well-known benchmark problems and are compared to those produced for the same problems by three other methods.