Authors:
César L. Alonso
1
;
José Luis Montaña
2
and
Cruz Enrique Borges
3
Affiliations:
1
Universidad de Oviedo, Spain
;
2
Universidad de Cantabria, Spain
;
3
Universidad de Deusto, Spain
Keyword(s):
Genetic Programming, Straight Line Program, Pfaffian Operator, Symbolic Regression.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolution Strategies
;
Evolutionary Computing
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
Abstract:
In this paper we propose a tool for controlling the complexity of Genetic Programming models. The tool is supported by the theory of Vapnik-Chervonekis dimension (VCD) and is combined with a novel representation of models named straight line program.
Experimental results, implemented on conventional algebraic structures (such as polynomials) and real problems, show that the empirical risk, penalized by suitable upper bounds for the Vapnik-Chervonenkis dimension, gives a generalization error smaller than the use of statistical conventional techniques such as Bayesian or Akaike information criteria.