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EUROGP
2004
Springer

Comparing Hybrid Systems to Design and Optimize Artificial Neural Networks

9 years 5 months ago
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 perceptrons: 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.
Pedro A. Castillo Valdivieso, Maribel Garcí
Added 20 Aug 2010
Updated 20 Aug 2010
Type Conference
Year 2004
Where EUROGP
Authors Pedro A. Castillo Valdivieso, Maribel García Arenas, Juan J. Merelo Guervós, Gustavo Romero, Fatima Rateb, Alberto Prieto
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