Model selection via Genetic Algorithms for RBF networks

Issue title: SBRN'02

Article type: Research Article

Authors: de Lacerda, Estefane G.M. | de Carvalho, André C.P.L.F. | Ludermir, Teresa B.

Affiliations: Federal University of Rio Grande do Norte, Brazil. E-mail: estefane@dca.ufrn.br | ICMC – University of São Paulo, São Carlos, SP, Brazil. E-mail: andre@icmc.usp.br

Abstract: This work addresses the problem of finding the adjustable parameters of a learning algorithm using Genetic Algorithms. This problem is also known as the model selection problem. In this paper, some model selection techniques (e.g., crossvalidation and bootstrap) are used as objective functions of a Genetic Algorithm. The Genetic Algorithm is modified in order to allow the efficient use of these objective functions by means of occam's razor, growing, and other heuristics. Some modifications explore intrinsic features of Genetic Algorithms, such as their ability to handle multiple and noise objective functions. The proposed techniques are very general and may be applied to a large range of learning algorithms.

Keywords: Genetic Algorithms, model selection, crossvalidation, holdout, bootstrap, RBF networks

Journal: Journal of Intelligent & Fuzzy Systems, vol. 13, no. 2-4, pp. 111-122, 2002/2003