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A methodology to train and improve artificial neural networks' weights and connections

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2 Author(s)
C. Zanchettin ; Federal University of Pernambuco, Recife, PE P. O. Box 7851, 50.732-970, BRA. phone: +55-81-2126-8430; fax: +55-81-2126-8438; email: ; T. B. Ludermir

This work presents a new methodology that integrates the heuristics Tabu search, simulated annealing, genetic algorithms and backpropagation in a pruning and constructive way. The approach obtained promising results in the simultaneous optimization of artificial neural network architecture and weights. The experiments were performed in four classification and one prediction problem.

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The 2006 IEEE International Joint Conference on Neural Network Proceedings

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