A novel similarity-based crossover for artificial neural network evolution

Abstract

This work presents an evolutionary approach for the optimization of neural networks design, based on the joint evolution of the topology and the connection weights, providing a novel similarity-based crossover that aims to overcome one of the major problems of this operator, known as the permutation problem. The approach has been implemented and applied to two benchmark classification problems in machine learning, and the experimental results, compared to those obtained by other works in the literature, show how it can produce compact neural networks with a satisfactory generalization capability. © 2010 Springer-Verlag.