Detecting and Pruning Introns for Faster Decision Tree Evolution

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

  author =       "Jeroen Eggermont and Joost N. Kok and 
                 Walter A. Kosters",
  title =        "Detecting and Pruning Introns for Faster Decision Tree
  booktitle =    "Parallel Problem Solving from Nature - PPSN VIII",
  year =         "2004",
  editor =       "Xin Yao and Edmund Burke and Jose A. Lozano and 
                 Jim Smith and Juan J. Merelo-Guerv\'os and 
                 John A. Bullinaria and Jonathan Rowe and 
                 Peter Ti\v{n}o Ata Kab\'an and Hans-Paul Schwefel",
  volume =       "3242",
  pages =        "1071--1080",
  series =       "LNCS",
  address =      "Birmingham, UK",
  publisher_address = "Berlin",
  month =        "18-22 " # sep,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, bloat",
  ISBN =         "3-540-23092-0",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1007/b100601",
  size =         "10 pages",
  abstract =     "We show how the understandability and speed of genetic
                 programming classification algorithms can be improved,
                 without affecting the classification accuracy. By
                 analysing the decision trees evolved we can remove the
                 unessential parts, called introns, from the discovered
                 decision trees. Since the resulting trees contain only
                 useful information they are smaller and easier to
                 understand. Moreover, by using these pruned decision
                 trees in a fitness cache we can significantly reduce
                 the number of unnecessary fitness calculations.",
  notes =        "PPSN-VIII",

Genetic Programming entries for Jeroen Eggermont Joost Kok Walter A Kosters