DepthLimited crossover in GP for classifier evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.4215

  author =       "Hajira Jabeen and Abdul Rauf Baig",
  title =        "DepthLimited crossover in GP for classifier
  journal =      "Computers in Human Behavior",
  year =         "2011",
  volume =       "27",
  number =       "5",
  pages =        "1475--1481",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming, Crossover,
                 Depth Limited, Bloat, Classification, Data mining",
  ISSN =         "0747-5632",
  URL =          "",
  DOI =          "doi:10.1016/j.chb.2010.10.011",
  size =         "7 pages",
  abstract =     "Genetic Programming (GP) provides a novel way of
                 classification with key features like transparency,
                 flexibility and versatility. Presence of these
                 properties makes GP a powerful tool for classifier
                 evolution. However, GP suffers from code bloat, which
                 is highly undesirable in case of classifier evolution.
                 In this paper, we have proposed an operator named
                 DepthLimited crossover. The proposed crossover does not
                 let trees increase in complexity while maintaining
                 diversity and efficient search during evolution. We
                 have compared performance of traditional GP with
                 DepthLimited crossover GP, on data classification
                 problems and found that DepthLimited crossover
                 technique provides compatible results without expanding
                 the search space beyond initial limits. The proposed
                 technique is found efficient in terms of classification
                 accuracy, reduced complexity of population and
                 simplicity of evolved classifiers.",

Genetic Programming entries for Hajira Jabeen Abdul Rauf Baig