Impacts of sampling strategies in tournament selection for genetic programming

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

  title =        "Impacts of sampling strategies in tournament selection
                 for genetic programming",
  author =       "Huayang Xie and Mengjie Zhang",
  journal =      "Soft Computing - A Fusion of Foundations,
                 Methodologies and Applications",
  year =         "2012",
  volume =       "16",
  number =       "4",
  pages =        "615--633",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1432-7643",
  DOI =          "doi:10.1007/s00500-011-0760-x",
  size =         "19 pages",
  abstract =     "Tournament selection is one of the most commonly used
                 parent selection schemes in genetic programming (GP).
                 While it has a number of advantages over other
                 selection schemes, it still has some issues that need
                 to be thoroughly investigated. Two of the issues are
                 associated with the sampling process from the
                 population into the tournament. The first one is the
                 so-called multi-sampled issue, where some individuals
                 in the population are picked up (sampled) many times to
                 form a tournament. The second one is the not-sampled
                 issue, meaning that some individuals are never picked
                 up when forming tournaments. In order to develop a more
                 effective selection scheme for GP, it is necessary to
                 understand the actual impacts of these issues in
                 standard tournament selection. This paper investigates
                 the behaviour of different sampling replacement
                 strategies through mathematical modelling, simulations
                 and empirical experiments. The results show that
                 different sampling replacement strategies have little
                 impact on selection pressure and cannot effectively
                 tune the selection pressure in dynamic evolution. In
                 order to conduct effective parent selection in GP,
                 research focuses should be on developing automatic and
                 dynamic selection pressure tuning methods instead of
                 alternative sampling replacement strategies. Although
                 GP is used in the empirical experiments, the findings
                 revealed in this paper are expected to be applicable to
                 other evolutionary algorithms.",
  affiliation =  "School of Engineering and Computer Science, Victoria
                 University of Wellington, Wellington, New Zealand",
  bibdate =      "2012-03-10",
  bibsource =    "DBLP,

Genetic Programming entries for Huayang Jason Xie Mengjie Zhang