Directing Crossover for Reduction of Bloat in GP

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "M. Terrio and M. I. Heywood",
  title =        "Directing Crossover for Reduction of Bloat in GP",
  booktitle =    "IEEE CCECE 2003: IEEE Canadian Conference on
                 Electrical and Computer Engineering",
  year =         "2002",
  editor =       "W. Kinsner and A. Seback and K. Ferens",
  pages =        "1111--1115",
  month =        "12-15 " # may,
  organisation = "IEEE Canada",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, Code Bloat",
  ISBN =         "0-7803-7515-7",
  URL =          "",
  URL =          "",
  abstract =     "A method is proposed to reduce the amount of inviable
                 code (or bloat) produced in individuals while searching
                 for a parsimonious solution under tree structured
                 genetic programming. Known as directed crossover, this
                 process involves the identification of highly fit nodes
                 to use as crossover points during operator application.
                 Three test problems, including medical data
                 classification, are used to assess the performance of
                 directed crossover when applied at various thresholds.
                 Results, collected over 1260 independent runs, identify
                 conditions under which directed crossover reduces code

Genetic Programming entries for M David Terrio Malcolm Heywood