Genetic Programming, Validation Sets, and Parsimony Pressure

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@InProceedings{eurogp06:GagneSchoenauerParizeauTomassini,
  author =       "Christian Gagn\'e and Marc Schoenauer and 
                 Marc Parizeau and Marco Tomassini",
  title =        "Genetic Programming, Validation Sets, and Parsimony
                 Pressure",
  editor =       "Pierre Collet and Marco Tomassini and Marc Ebner and 
                 Steven Gustafson and Anik\'o Ek\'art",
  booktitle =    "Proceedings of the 9th European Conference on Genetic
                 Programming",
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "3905",
  year =         "2006",
  address =      "Budapest, Hungary",
  month =        "10 - 12 " # apr,
  organisation = "EvoNet",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-33143-3",
  pages =        "109--120",
  URL =          "http://hal.ccsd.cnrs.fr/docs/00/05/44/78/PDF/gagne-paper.pdf",
  URL =          "http://hal.inria.fr/inria-00000996/en/",
  URL =          "http://link.springer.de/link/service/series/0558/papers/3905/39050109.pdf",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  abstract =     "Fitness functions based on test cases are very common
                 in Genetic Programming (GP). This process can be
                 assimilated to a learning task, with the inference of
                 models from a limited number of samples. This paper is
                 an investigation on two methods to improve
                 generalisation in GP-based learning: 1) the selection
                 of the best-of-run individuals using a three data sets
                 methodology, and 2) the application of parsimony
                 pressure in order to reduce the complexity of the
                 solutions. Results using GP in a binary classification
                 setup show that while the accuracy on the test sets is
                 preserved, with less variances compared to baseline
                 results, the mean tree size obtained with the tested
                 methods is significantly reduced.",
  notes =        "Part of \cite{collet:2006:GP} EuroGP'2006 held in
                 conjunction with EvoCOP2006 and EvoWorkshops2006

                 Also known as
                 \cite{oai:hal.ccsd.cnrs.fr:inria-00000996_v1}

                 overfitting, regularisation, V-C dimension, MDL, UCI,
                 fit the noise.",
}

Genetic Programming entries for Christian Gagne Marc Schoenauer Marc Parizeau Marco Tomassini