A comparison of genetic programming variants for data classification

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "J. Eggermont and A. E. Eiben and J. I. {van Hemert}",
  title =        "A comparison of genetic programming variants for data
  booktitle =    "Proceedings of the Eleventh Belgium/Netherlands
                 Conference on Artificial Intelligence (BNAIC'99)",
  year =         "1999",
  editor =       "Eric Postma and Marc Gyssens",
  pages =        "253--254",
  address =      "Kasteel Vaeshartelt, Maastricht, Holland",
  month =        "3-4 " # nov,
  organisation = "BNVKI, Dutch and the Belgian AI Association",
  keywords =     "genetic algorithms, genetic programming, data mining,
  URL =          "http://www.liacs.nl/~jeggermo/publications/bnaic00.ps.gz",
  URL =          "http://www.vanhemert.co.uk/publications/bnaic99.shortpaper.Comparing_genetic_programming_variants_for_data_classification.ps.gz",
  size =         "2 pages",
  abstract =     "This article is a combined summary of two papers
                 written by the authors. Binary data classification
                 problems (with exactly two disjoint classes) form an
                 important application area of machine learning
                 techniques, in particular genetic programming (GP). We
                 compare a number of different variants of GP applied to
                 such problems whereby we investigate the effect of two
                 significant changes in a fixed GP setup in combination
                 with two different evolutionary models",
  notes =        "resubmission of \cite{EEH99b}

Genetic Programming entries for Jeroen Eggermont Gusz Eiben Jano I van Hemert