Comparing evolutionary hybrid systems for design and optimization of multilayer perceptron structure along training parameters |
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Authors: | P.A. Castillo J.J. Merelo G. Romero |
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Affiliation: | a Departamento de Arquitectura y Tecnología de Computadoras, ETSI Informática, Universidad de Granada, Spain b Departamento de Informática de la Universidad de Jaén, Escuela Politécnica Superior, Universidad de Jaén, Avda. Madrid, 35, E. 23071 Jaén, Spain |
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Abstract: | In this paper we present a comparative study of several methods that combine evolutionary algorithms and local search to optimize multilayer perceptrons: A method that optimizes the architecture and initial weights of multilayer perceptrons; another that searches for training algorithm parameters, and finally, a co-evolutionary algorithm, introduced here, that handles the architecture, the network’s initial weights and the training algorithm parameters. Our aim is to determine how the co-evolutive method can obtain better results from the point of view of running time and classification ability. Experimental results show that the co-evolutionary method obtains similar or better results than the other approaches, requiring far less training epochs and thus, reducing running time. |
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Keywords: | Evolutionary algorithms Artificial neural networks Multilayer perceptrons Hybrid algorithms Optimization G-Prop Co-evolution |
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