Comparing hybrid systems to design and optimize artificial neural networks

Abstract

In this paper we conduct a comparative study between hybrid methods to optimize multilayer perceptions a model that optimizes the architecture and initial weights of multilayer perceptrons; a parallel approach to optimize the architecture and initial weights of multilayer perceptrons; a method that searches for the parameters of the training algorithm, and an approach for cooperative co-evolutionary optimization of multilayer perceptrons. Obtained results show that a co-evolutionary model obtains similar or better results than specialized approaches, needing much less training epochs and thus using much less simulation time. © Springer-Verlag 2004.