Distribution of the search of evolutionary product unit neural networks for classification

This research is about the distribution of processing involved in the search for the best product-unit neural network (PUNN) models [Durbin, 1990] [Martínez-Estud illo, 2006A], using evolutionary algorithms, EAs. A cluster of computers [Buyya, 1999] will be used to carry out the distribution of this processing. Many different types of neural network architectures have been used, but the most popular one has been the single-hidden-layer feedforward network. Amongst the numerous approaches that use neural networks in classification problems, we focus our attention on ev olutionary artificial neural networks (EANNs). EANNs have been a key research area in the past decade pr oviding an improved platfo rm for optimizing network performance and architecture (number of hidden nodes and number of connections) simultaneously.