Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{1068309, author = "Sylvain Gelly and Olivier Teytaud and Nicolas Bredeche and Marc Schoenauer", title = "A statistical learning theory approach of bloat", booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on Genetic and evolutionary computation", year = "2005", editor = "Hans-Georg Beyer and Una-May O'Reilly and Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and Eric W. Bonabeau and Erick Cantu-Paz and Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and Edwin D. {de Jong} and Hod Lipson and Xavier Llora and Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and Terence Soule and Andy M. Tyrrell and Jean-Paul Watson and Eckart Zitzler", volume = "2", ISBN = "1-59593-010-8", pages = "1783--1784", address = "Washington DC, USA", URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1783.pdf", URL = "http://www.lri.fr/~teytaud/eabloat.pdf", broken = "http://www.lri.fr/~teytaud/eabloat/eabloat.html", DOI = "doi:10.1145/1068009.1068309", publisher = "ACM Press", publisher_address = "New York, NY, 10286-1405, USA", month = "25-29 " # jun, organisation = "ACM SIGEVO (formerly ISGEC)", keywords = "genetic algorithms, genetic programming, Poster, code bloat, code growth, reliability, statistical learning theory, theory", size = "2 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-Chervonenkis 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 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 more complicated modification of the fitness is proposed that theoretically avoids unnecessary bloat while nevertheless preserving the Universal Consistency. Full paper available at http://www.lri.fr/~teytaud/longBloat.pdf \cite{gelly:2005:longBloat}", notes = "GECCO-2005 A joint meeting of the fourteenth international conference on genetic algorithms (ICGA-2005) and the tenth annual genetic programming conference (GP-2005). ACM Order Number 910052 eabloat.pdf is substantially more complete than poster in GECCO proceedings", }

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