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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Memetic Algorithms
verfasst von : Natalio Krasnogor, Alberto Aragón, Joaquín Pacheco
Erschienen in: Metaheuristic Procedures for Training Neutral Networks
Verlag: Springer US
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This chapter introduces and analyzes a memetic algorithm approach for the training of artificial neural networks, more specifically multilayer perceptrons. Our memetic algorithm is proposed as an alternative to gradient search methods, such as
backpropagation
, which have shown limitations when dealing with rugged landscapes with many poor local optimae. The aim of our work is to design a training strategy that is able to cope with difficult error manyfolds, and to quickly deliver trained neural networks that produce small errors. A method such as the one we proposed might also be used as an “online” training strategy.