Adaptation, Performance and Vapnik-Chervonenkis Dimension of Straight Line Programs

Abstract. We discuss here empirical comparation between model selection methods based on Linear Genetic Programming. Two statistical methods are compared: model selection based on Empirical Risk Minimization (ERM) and model selection based on Structural Risk Minimization (SRM). For this purpose we have identified the main components which determine the capacity of some linear structures as classifiers showing an upper bound for the Vapnik-Chervonenkis (VC) dimension of classes of programs representing linear code defined by arithmetic computations and sign tests. This upper bound is used to define a fitness based on VC regularization that performs significantly better than the fitness based on empirical risk. Key words: Genetic Programming, Linear Genetic Programming, VapnikChervonenkis dimension

• Type: Conference
• Year: 2009
• Where: EUROGP
• Authors: José Luis Montaña, César Luis Alonso, Cruz E. Borges, José Luis Crespo
• Keywords: Artificial Intelligence, Empirical Risk, EUROGP 2009, Linear Genetic Programming, Model Selection