Model selection in genetic programming

Authors:
Cruz E. Borges;César L. Alonso;José L. Montaña
Affiliations:
Universidad de Cantabria, Santander, Spain;Universidad de Oviedo, Gijon, Spain;Universidad de Cantabria, Santander, Spain
Venue:
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Year:
2010

Citing 3
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Abstract

In this paper we discuss the problem of model selection in Genetic Programming. We present empirical comparisons between classical statistical methods (AIC, BIC) adapted to Genetic Programming and the Structural Risk Minimization method (SRM) based on Vapnik-Chervonenkis theory (VC), for symbolic regression problems with added noise. We also introduce a new model complexity measure for the SRM method that tries to measure the non-linearity of the model. The experimentation suggests practical advantages of using VC-based model selection with the new complexity measure, when using genetic training.