Depth-Control Strategies for Crossover in Tree-based Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Huayang Xie and Mengjie Zhang",
  title =        "Depth-Control Strategies for Crossover in Tree-based
                 Genetic Programming",
  journal =      "Soft Computing - A Fusion of Foundations,
                 Methodologies and Applications",
  year =         "2011",
  volume =       "15",
  number =       "9",
  pages =        "1865--1878",
  keywords =     "genetic algorithms, genetic programming, Crossover,
                 tree-based genetic programming",
  publisher =    "Springer",
  ISSN =         "1432-7643",
  DOI =          "doi:10.1007/s00500-011-0700-9",
  size =         "14 pages",
  abstract =     "The standard subtree crossover operator in the
                 tree-based genetic programming (GP) has been considered
                 as problematic. In order to improve the standard
                 subtree crossover, controlling depth of crossover
                 points becomes a research topic. However, the existence
                 of many different and inconsistent crossover
                 depth-control schemes and the possibility of many other
                 depth-control schemes make the identification of good
                 depth-control schemes a challenging problem. This paper
                 aims to investigate general heuristics for making good
                 depth-control schemes for crossover in tree-based GP.
                 It analyses the patterns of depth of crossover points
                 in good predecessor programs of five GP systems that
                 use the standard subtree crossover and four
                 approximations of the optimal crossover operator on
                 three problems in different domains. The analysis
                 results show that an effective depth-control scheme is
                 problem-dependent and evolutionary stage-dependent, and
                 that good crossover events have a strong preference for
                 roots and (less strongly) bottoms of parent program
                 trees. The results also show that some ranges of depths
                 between the roots and the bottoms are also preferred,
                 suggesting that unequal-depth-selection-probability
                 strategies are better than
                 equal-depth-selection-probability strategies.",
  affiliation =  "School of Engineering and Computer Science, Victoria
                 University of Wellington, Wellington, New Zealand",

Genetic Programming entries for Huayang Jason Xie Mengjie Zhang