On Improving Genetic Programming for Symbolic Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@InProceedings{gustafson:2005:CEC,
  author =       "Steven Gustafson and Edmund K. Burke and 
                 Natalio Krasnogor",
  title =        "On Improving Genetic Programming for Symbolic
                 Regression",
  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 =       "1",
  pages =        "912--919",
  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",
  abstract =     "reports an improvement to genetic programming (GP)
                 search for the symbolic regression domain, based on an
                 analysis of dissimilarity and mating. GP search is
                 generally difficult to characterise for this domain,
                 preventing well motivated algorithmic improvements. We
                 first examine the ability of various solutions to
                 contribute to the search process. Further analysis
                 highlights the numerous solutions produced during
                 search with no change to solution quality. A simple
                 algorithmic enhancement is made that reduces these
                 events and produces a statistically significant
                 improvement in solution quality. We conclude by
                 verifying the generalisability of these results on
                 several other regression instances.",
  notes =        "CEC2005 - A joint meeting of the IEEE, the IEE, and
                 the EPS.",
}

Genetic Programming entries for Steven M Gustafson Edmund Burke Natalio Krasnogor