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.

• Type: Conference
• Year: 2010
• Where: GECCO
• Authors: Cruz E. Borges, César Luis Alonso, José L. Montaña
• Keywords: Empirical Comparisons, GECCO 2010, Model Selection, Optimization, VC-based Model Selection