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JIFS
2002

Model selection via Genetic Algorithms for RBF networks

9 years 1 months ago
Model selection via Genetic Algorithms for RBF networks
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.
Estefane G. M. de Lacerda, André Carlos Pon
Added 22 Dec 2010
Updated 22 Dec 2010
Type Journal
Year 2002
Where JIFS
Authors Estefane G. M. de Lacerda, André Carlos Ponce Leon Ferreira de Carvalho, Teresa Bernarda Ludermir
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