SYMBIONT: a cooperative evolutionary model for evolving artificial neural networks for classification

Authors:
Nicolás García-Pedrajas;César Hervás-Martínez;José Muñoz-Pérez
Affiliations:
Dept. of Computing and Numerical Analysis, Escuela Politécnica Superior, University of Córdoba;Dept. of Computing and Numerical Analysis, Escuela Politécnica Superior, University of Córdoba;Dept. of Languages and Computer Science, Escuela Superior de Ingeniería Informática, University of Málaga
Venue:
Technologies for constructing intelligent systems
Year:
2002

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Abstract

A new cooperative evolutionary model, called Symbiont, for evolving artificial neural networks is presented in this paper. This model is based on the idea of developing subnetworks, called nodules, that must cooperate to form a solution, instead of evolving a complete network. The performance of the model in solving two real-world problems of classification is compared with a multilayer perceptron trained using back-propagation. Symbiont has proved to show better generalization than the multilayer perceptron and to evolve smaller networks.