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Erschienen in:
2003 | OriginalPaper | Buchkapitel
Discovering Efficient Learning Rules for Feedforward Neural Networks Using Genetic Programming
verfasst von : Amr Radi, Riccardo Poli
Erschienen in: Recent Advances in Intelligent Paradigms and Applications
Verlag: Physica-Verlag HD
Enthalten in: Professional Book Archive
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The Standard BackPropagation (SBP) algorithm is the most widely known and used learning method for training neural networks. Unfortunately, SBP suffers from several problems such as sensitivity to the initial conditions and very slow convergence. Here we describe how we used Genetic Programming, a search algorithm inspired by Darwinian evolution, to discover new supervised learning algorithms for neural networks which can overcome some of these problems. Comparing our new algorithms with SBP on different problems we show that these are faster, are more stable and have greater feature extracting capabilities.