Automatic Neural Net Design by Means of a Symbiotic Co-evolutionary Algorithm

Authors:
Elisabet Parras-Gutierrez;Víctor M. Rivas;Maria Jose Jesus
Affiliations:
Departament of Computer Sciences, University of Jaen,;Departament of Computer Sciences, University of Jaen,;Departament of Computer Sciences, University of Jaen,
Venue:
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Year:
2008

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Abstract

One of the most important issues that must be taken in mind to optimize the design and the generalization abilities of trained artificial neural networks (ANN) is the architecture of the net. In this paper Symbiotic_RBF is proposed, a method to do automatically the process to design models for classification using symbiosis. For it, there are two populations who evolve together by means of coevolution. One of the populations is the method EvRBF, which provides the design of radial basis function neural nets by means of evolutionary algorithms. The second population evolves sets of parameters for the method EvRBF, being every individual of the population a configuration of parameters for the method. Thus, the main goal of Symbiotic_RBF is to find a suitable configuration of parameters necessary for the method EvRBF, which is adapted automatically to every problem.