Universal Consistency and Bloat in GP
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{oai:hal.archives-ouvertes.fr:inria-00112840_v1,
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author = "Sylvain Gelly and Olivier Teytaud and
Nicolas Bredeche and Marc Schoenauer",
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title = "Universal Consistency and Bloat in {GP}",
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title_2 = "Some theoretical considerations about Genetic
Programming from a Statistical Learning Theory
viewpoint",
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journal = "Revue d'Intelligence Artificielle",
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year = "2006",
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volume = "20",
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number = "6",
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pages = "805--827",
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note = "Issue on New Methods in Machine Learning. Theory and
Applications",
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publisher = "HAL - CCSd - CNRS",
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annote = "Sylvain Gelly ",
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bibsource = "OAI-PMH server at hal.archives-ouvertes.fr",
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contributor = "Sylvain Gelly ",
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identifier = "inria-00112840 (version 1)",
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oai = "oai:hal.archives-ouvertes.fr:inria-00112840_v1",
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keywords = "genetic algorithms, genetic programming, Computer
Science/Learning, Mathematics/Optimization and Control,
statistical learning theory, symbolic regression,
universal consistency, bloat",
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ISSN = "0992-499X",
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URL = "http://hal.inria.fr/docs/00/11/28/40/PDF/riabloat.pdf",
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URL = "http://hal.inria.fr/inria-00112840/en/",
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broken = "http://ria.revuesonline.com/article.jsp?articleId=8936",
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broken = "doi:10.3166/ria.20.805-827",
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size = "23 pages",
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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.",
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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.",
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notes = "in english",
- }
Genetic Programming entries for
Sylvain Gelly
Olivier Teytaud
Nicolas Bredeche
Marc Schoenauer
Citations