Model Complexity Control in Straight Line Program Genetic Programming

César L. Alonso, José Luis Montaña, Cruz Enrique Borges


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.


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Paper Citation

in Harvard Style

L. Alonso C., Luis Montaña J. and Enrique Borges C. (2013). Model Complexity Control in Straight Line Program Genetic Programming . In Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2013) ISBN 978-989-8565-77-8, pages 25-36. DOI: 10.5220/0004554100250036

in Bibtex Style

author={César L. Alonso and José Luis Montaña and Cruz Enrique Borges},
title={Model Complexity Control in Straight Line Program Genetic Programming},
booktitle={Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2013)},

in EndNote Style

JO - Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2013)
TI - Model Complexity Control in Straight Line Program Genetic Programming
SN - 978-989-8565-77-8
AU - L. Alonso C.
AU - Luis Montaña J.
AU - Enrique Borges C.
PY - 2013
SP - 25
EP - 36
DO - 10.5220/0004554100250036