On Improving Genetic Programming for Symbolic Regression

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

  author =       "Steven Gustafson and Edmund K. Burke and 
                 Natalio Krasnogor",
  title =        "On Improving Genetic Programming for Symbolic
  booktitle =    "Proceedings of the 2005 IEEE Congress on Evolutionary
  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",
  DOI =          "doi:10.1109/CEC.2005.1554780",
  abstract =     "This paper 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
  notes =        "CEC2005 - A joint meeting of the IEEE, the IEE, and
                 the EPS.",

Genetic Programming entries for Steven M Gustafson Edmund Burke Natalio Krasnogor