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GECCO
2010
Springer

Model selection in genetic programming

9 years 4 months ago
Model selection in genetic programming
Abstract. We discuss the problem of model selection in Genetic Programming using the framework provided by Statistical Learning Theory, i.e. Vapnik-Chervonenkis theory (VC). We present empirical comparisons between classical statistical methods (AIC, BIC) for model selection and the Structural Risk Minimization method (based on VC-theory) for symbolic regression problems. Empirical comparisons of different methods for model selection suggest practical advantages of using VC-based model selection when using genetic training.
Cruz E. Borges, César Luis Alonso, Jos&eacu
Added 19 Jul 2010
Updated 19 Jul 2010
Type Conference
Year 2010
Where GECCO
Authors Cruz E. Borges, César Luis Alonso, José L. Montaña
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