Teams of Genetic Predictors for Inverse Problem Solving

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

@InProceedings{eurogp:Defoin-PlatelCCC05,
  author =       "Michael Defoin-Platel and Malik Chami and 
                 Manuel Clergue and Philippe Collard",
  editor =       "Maarten Keijzer and Andrea Tettamanzi and 
                 Pierre Collet and Jano I. {van Hemert} and Marco Tomassini",
  title =        "Teams of Genetic Predictors for Inverse Problem
                 Solving",
  booktitle =    "Proceedings of the 8th European Conference on Genetic
                 Programming",
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "3447",
  year =         "2005",
  address =      "Lausanne, Switzerland",
  month =        "30 " # mar # " - 1 " # apr,
  organisation = "EvoNet",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-25436-6",
  pages =        "341--350",
  size =         "10",
  URL =          "http://www.obs-vlfr.fr/LOV/OMT/fichiers_PDF/Defoin_and_Chami_LNCS_05.pdf",
  DOI =          "doi:10.1007/b107383",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  abstract =     "Genetic Programming (GP) has been shown to be a good
                 method of predicting functions that solve inverse
                 problems. In this context, a solution given by GP
                 generally consists of a sole predictor. In contrast,
                 Stack-based GP systems manipulate structures containing
                 several predictors, which can be considered as teams of
                 predictors. Work in Machine Learning reports that
                 combining predictors gives good results in terms of
                 both quality and robustness. In this paper, we use
                 Stack-based GP to study different cooperations between
                 predictors. First, preliminary tests and parameter
                 tuning are performed on two GP benchmarks. Then, the
                 system is applied to a real-world inverse problem. A
                 comparative study with standard methods has shown
                 limits and advantages of teams prediction, leading to
                 encourage the use of combinations taking into account
                 the response quality of each team member.",
  notes =        "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in
                 conjunction with EvoCOP2005 and EvoWorkshops2005",
}

Genetic Programming entries for Michael Defoin Platel Malik Chami Manuel Clergue Philippe Collard

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