Training Binary GP Classifiers Efficiently: a Pareto-coevolutionary Approach

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

@InProceedings{eurogp07:lemczyk,
  author =       "Michal Lemczyk and Malcolm I. Heywood",
  title =        "Training Binary GP Classifiers Efficiently: a
                 Pareto-coevolutionary Approach",
  editor =       "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and 
                 Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar",
  booktitle =    "Proceedings of the 10th European Conference on Genetic
                 Programming",
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "4445",
  year =         "2007",
  address =      "Valencia, Spain",
  month =        "11-13 " # apr,
  pages =        "229--240",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-71602-5",
  isbn13 =       "978-3-540-71602-0",
  DOI =          "doi:10.1007/978-3-540-71605-1_21",
  abstract =     "The conversion and extension of the Incremental
                 Pareto-Coevolution Archive algorithm (IPCA) into the
                 domain of Genetic Programming classification is
                 presented. In particular, the coevolutionary aspect of
                 the IPCA algorithm is used to simultaneously evolve a
                 subset of the training data that provides distinctions
                 between candidate classifiers. Empirical results
                 indicate that such a scheme significantly reduces the
                 computational overhead of fitness evaluation on large
                 binary classification data sets. Moreover, unlike the
                 performance of GP classifiers trained using alternative
                 subset selection algorithms, the proposed
                 Pareto-coevolutionary approach is able to match or
                 better the classification performance of GP trained
                 over all training exemplars. Finally, problem
                 decomposition appears as a natural consequence of
                 assuming a Pareto model for coevolution. In order to
                 make use of this property a voting scheme is used to
                 integrate the results of all classifiers from the
                 Pareto front, post training.",
  notes =        "Part of \cite{ebner:2007:GP} EuroGP'2007 held in
                 conjunction with EvoCOP2007, EvoBIO2007 and
                 EvoWorkshops2007",
}

Genetic Programming entries for Michal Lemczyk Malcolm Heywood

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