A statistical learning approach to bloat and universal consistency in genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Misc{oai:eprints.pascal-network.org:1586,

• author = "Olivier Teytaud and Marc Schoenauer and Sylvain Gelly and Nicolas Bredeche",
• title = "A statistical learning approach to bloat and universal consistency in genetic programming",
• year = "2005",
• abstract = "Universal Consistency, the convergence to the minimum possible error rate in learning through genetic programming (GP), and Code bloat, the excessive increase of code size, are important issues in GP. This paper proposes a theoretical analysis of universal consistency and code bloat in the framework of symbolic regression in GP, from the viewpoint of Statistical Learning Theory, a well grounded mathematical toolbox for Machine Learning. Two kinds of bloat must be distinguished in that context, depending whether the target function has finite description length or not. Then, the Vapnik-Chervonenkis dimension of programs is computed, and we prove that a parsimonious fitness ensures Universal Consistency (i.e. the fact that the solution minimising the empirical error does converge to the best possible error when the number of examples goes to infinity). However, it is proved that the standard method consisting in choosing a maximal program size depending on the number of examples might still result in programs of infinitely increasing size with their accuracy; a fitness biased by parsimony pressure is proposed. This fitness avoids unnecessary bloat while nevertheless preserving the Universal Consistency.",
• bibsource = "OAI-PMH server at eprints.pascal-network.org",
• oai = "oai:eprints.pascal-network.org:1586",
• URL = "http://hal.archives-ouvertes.fr/docs/00/04/19/72/PDF/antibloatGecco2005_long_version.pdf",
• URL = "http://eprints.pascal-network.org/archive/00001586/",
• URL = "http://eprints.pascal-network.org/archive/00001586/01/eabloat.pdf",
• keywords = "genetic algorithms, genetic programming, VC, Learning/Statistics \& Optimisation",
• size = "8 pages",
• notes = "pascal-network.org URLs appear broken June 2015. See also GECCO 2005 \cite{1068309}. Also known as \cite{oai:hal.ccsd.cnrs.fr:inria-00000549_v1}",

}

Genetic Programming entries for Olivier Teytaud Marc Schoenauer Sylvain Gelly Nicolas Bredeche

Citations