Single Parent Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{ashlock:2005:CECw,
  author =       "Wendy Ashlock and Dan Ashlock",
  title =        "Single Parent Genetic Programming",
  booktitle =    "Proceedings of the 2005 IEEE Congress on Evolutionary
                 Computation",
  year =         "2005",
  editor =       "David Corne and Zbigniew Michalewicz and 
                 Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and 
                 Garrison Greenwood and Tan Kay Chen and 
                 Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and 
                 Jennifier Willies and Juan J. Merelo Guervos and 
                 Eugene Eberbach and Bob McKay and Alastair Channon and 
                 Ashutosh Tiwari and L. Gwenn Volkert and 
                 Dan Ashlock and Marc Schoenauer",
  volume =       "2",
  pages =        "1172--1179",
  address =      "Edinburgh, UK",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "2-5 " # sep,
  organisation = "IEEE Computational Intelligence Society, Institution
                 of Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7803-9363-5",
  DOI =          "doi:10.1109/CEC.2005.1554823",
  abstract =     "The most controversial part of genetic programming is
                 its highly disruptive and potentially innovative
                 subtree crossover operator. The clearest problem with
                 the crossover operator is its potential to induce
                 defensive metaselection for large parse trees, a
                 process usually termed 'bloat'. Single parent genetic
                 programming is a form of genetic programming in which
                 bloat is reduced by doing subtree crossover with a
                 fixed population of ancestor trees. Analysis of mean
                 tree size growth demonstrates that this fixed and
                 limited set of crossover partners provides implicit,
                 automatic control on tree size in the evolving
                 population, reducing the need for additionally
                 disruptive trimming of large trees. The choice of
                 ancestor trees can also incorporate expert knowledge
                 into the genetic programming system. The system is
                 tested on four problems: plus-one-recall-store (PORS),
                 odd parity, plus-times-half (PTH) and a bioinformatic
                 model fitting problem (NIPs). The effectiveness of the
                 technique varies with the problem and choice of
                 ancestor set. At the extremes, improvements in time to
                 solution in excess of 4700-fold were observed for the
                 PORS problem, and no significant improvements for the
                 PTH problem were observed.",
  notes =        "CEC2005 - A joint meeting of the IEEE, the IEE, and
                 the EPS.",
}

Genetic Programming entries for Wendy Ashlock Daniel Ashlock

Citations