Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{DBLP:conf/cfap/GellyTBS05, author = "Sylvain Gelly and Olivier Teytaud and Nicolas Bredeche and Marc Schoenauer", title = "Apprentissage statistique et programmation g{\'e}n{\'e}tique: la croissance du code est-elle in{\'e}vitable?", booktitle = "Actes de CAP 05, Conf{\'e}rence francophone sur l'apprentissage automatique", year = "2005", editor = "Fran\c{c}ois Denis", pages = "163--178", address = "Nice, France", month = "31 " # may # "-3 " # jun, publisher = "PUG", note = "A Statistical Learning Theory Approach of Bloat", bibsource = "DBLP, http://dblp.uni-trier.de", keywords = "genetic algorithms, genetic programming, VC, Bloat", URL = "http://www.lri.fr/~gelly/paper/bloatCap2005.pdf", size = "16 pages", abstract = "Code bloat, the excessive increase of code size, is an important issue in Genetic Programming (GP). This paper proposes a theoretical analysis of 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 lies in the search space or not. Then, important mathematical results are proved using classical results from Statistical Learning. Namely, the Vapnik-Cervonenkis dimension of programs is computed, and further results from Statistical Learning allow to prove that a parsimonious fitness ensures Universal Consistency (the solution minimising the empirical error does converge to the best possible error when the number of samples goes to infinity). However, it is proved that the standard method consisting in choosing a maximal program size depending on the number of samples might still result in programs of infinitely increasing size with their accuracy ; a more complicated modification of the fitness is proposed that theoretically avoids unnecessary bloat while nevertheless preserving the Universal Consistency.", notes = "CAP 2005 http://www.lif.univ-mrs.fr/~fdenis/cap05/ In english. an improved version of \cite{gelly:2005:longBloat} Part of DBLP:conf/cfap/2005 see also oai:CiteSeerX.psu:10.1.1.696.8127 http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.696.8127 https://hal.inria.fr/inria-00000546/document/", }

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