Universal Consistency and Bloat in GP

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{oai:hal.archives-ouvertes.fr:inria-00112840_v1,

• author = "Sylvain Gelly and Olivier Teytaud and Nicolas Bredeche and Marc Schoenauer",
• title = "Universal Consistency and Bloat in {GP}",
• title_2 = "Some theoretical considerations about Genetic Programming from a Statistical Learning Theory viewpoint",
• journal = "Revue d'Intelligence Artificielle",
• year = "2006",
• volume = "20",
• number = "6",
• pages = "805--827",
• note = "Issue on New Methods in Machine Learning. Theory and Applications",
• publisher = "HAL - CCSd - CNRS",
• annote = "Sylvain Gelly ",
• bibsource = "OAI-PMH server at hal.archives-ouvertes.fr",
• contributor = "Sylvain Gelly ",
• identifier = "inria-00112840 (version 1)",
• oai = "oai:hal.archives-ouvertes.fr:inria-00112840_v1",
• keywords = "genetic algorithms, genetic programming, Computer Science/Learning, Mathematics/Optimization and Control, statistical learning theory, symbolic regression, universal consistency, bloat",
• ISSN = "0992-499X",
• URL = "http://hal.inria.fr/docs/00/11/28/40/PDF/riabloat.pdf",
• URL = "http://hal.inria.fr/inria-00112840/en/",
• broken = "http://ria.revuesonline.com/article.jsp?articleId=8936",
• broken = "doi:10.3166/ria.20.805-827",
• size = "23 pages",
• resume = "Dans cet article, nous proposons une etude de la Programmation Genetique (PG) du point de vue de la theorie de l'Apprentissage Statistique dans le cadre de la regression symbolique. En particulier, nous nous sommes interesses a la consistence universelle en PG, c'est-adire la convergence presque sure vers l'erreur bayesienne a mesure que le nombre d'exemples augmente, ainsi qu'au probleme bien connu en PG de la croissance incontrolee de la taille du code (i.e. le {"}bloat{"}). Les resultats que nous avons obtenus montrent d'une part que l'on peut identifier plusieurs types de bloat et d'autre part que la consistence universelle et l'absence de bloat peuvent etre obtenues sous certaines conditions. Nous proposons finalement une methode ad hoc evitant justement le bloat tout en garantissant la consistence universelle.",
• abstract = "In this paper, we provide an analysis of Genetic Programming (GP) from the Statistical Learning Theory viewpoint in the scope of symbolic regression. Firstly, we are interested in 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, and secondly, we focus our attention on the uncontrolled growth of program length (i.e. bloat), which is a well-known problem in GP. Results show that (1) several kinds of code bloats may be identified and that (2) Universal consistency can be obtained as well as avoiding bloat under some conditions. We conclude by describing an ad hoc method that makes it possible simultaneously to avoid bloat and to ensure universal consistency.",
• notes = "in english",

}

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

Citations