Genetic Programming, Validation Sets, and Parsimony Pressure

Created by W.Langdon from gp-bibliography.bib Revision:1.4472

  title =        "Genetic Programming, Validation Sets, and Parsimony
  author =       "Christian Gagn{\'e} and Marc Schoenauer and 
                 Marc Parizeau and Marco Tomassini",
  publisher =    "HAL - CCSd - CNRS",
  year =         "2006",
  month =        jan # "~09",
  institution =  "l'Equipe TAO INRIA Futurs",
  type =         "ARTCOLLOQUE",
  number =       "inria-00000996",
  address =      "LRI Bat. 490, Universite Paris Sud, 91405 Orsay CEDEX,
  annote =       "Christian Gagn{\'e} ",
  bibsource =    "OAI-PMH server at",
  contributor =  "Christian Gagn{\'e} ",
  identifier =   "inria-00000996 (version 1)",
  oai =          "",
  keywords =     "genetic algorithms, genetic programming, Computer
  URL =          "",
  URL =          "",
  URL =          "",
  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
                 generalization 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.",
  size =         "12 pages",

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