An Analysis of Depth of Crossover Points in Tree-Based Genetic Programming

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

  author =       "Huayang Xie and Mengjie Zhang and Peter Andreae",
  title =        "An Analysis of Depth of Crossover Points in Tree-Based
                 Genetic Programming",
  booktitle =    "2007 IEEE Congress on Evolutionary Computation",
  year =         "2007",
  editor =       "Dipti Srinivasan and Lipo Wang",
  pages =        "4561--4568",
  address =      "Singapore",
  month =        "25-28 " # sep,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  ISBN =         "1-4244-1340-0",
  file =         "1696.pdf",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CEC.2007.4425069",
  abstract =     "The standard crossover operator in tree-based Genetic
                 Programming (GP) is problematic in that it is most
                 often destructive. Selecting crossover points with an
                 implicit bias towards the leaves of a program tree
                 aggravates its destructiveness and causes the code
                 bloat problem in GP. Therefore, a common view has been
                 developed that adjusting the depth of crossover points
                 to eliminate the bias can improve GP performance, and
                 many attempts have been made to create effective
                 crossover operators according to this view. As there
                 are a large number of possible depth-control
                 strategies, it is very difficult to identify the
                 strategy that provides the most significant improvement
                 in performance. This paper explores depth-control
                 strategies by analysing the depth of crossover points
                 in evolutionary process logs of five different GP
                 systems on problems in three different domains. It
                 concludes that controlling the depth of crossover
                 points is an evolutionary stage dependent and problem
                 dependent task, and obtaining a significant performance
                 improvement is not trivial.",
  notes =        "CEC 2007 - A joint meeting of the IEEE, the EPS, and
                 the IET.

                 IEEE Catalog Number: 07TH8963C",

Genetic Programming entries for Huayang Jason Xie Mengjie Zhang Peter Andreae