Hybrid evolutionary algorithm for multilayer perceptron networks with competitive performance

Hybrid models combining neural networks and genetic algorithms have been studied recently with the goal of achieving either better performance of the resulting network or faster training. In this paper we deal with variants of genetic learning applied for the structure optimization and weights evolution of multi-layer perceptron networks. Several genetic operators are tested, including memetic-type local search, that produce good results in terms of network performace. It is shown, that combining evolutionary algorithms with neural networks can lead to better results than relying on neural networks alone in terms of the quality of the solution (both training and generalization error). Comparison to gradient algorithms in terms of time complexity is discussed which does not bring overly optimistic results sometimes met in literature.