Comparing Tree Depth Limits and Resource-Limited GP

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

  author =       "Sara Silva and Ernesto Costa",
  title =        "Comparing Tree Depth Limits and Resource-Limited GP",
  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 =        "920--927",
  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.2005.1554781",
  abstract =     "In this paper we compare two different approaches for
                 controlling bloat in genetic programming, tree depth
                 limits and resource-limited GP. Tree depth limits
                 operate at the individual level, avoiding excessive
                 code growth by imposing a maximum depth to each
                 individual. Resource-limited GP is a new technique that
                 operates at the population level, limiting the total
                 amount of resources the entire population can use. We
                 compare their dynamics and performance on three
                 problems: symbolic regression, even parity, and
                 artificial ant. The results suggest that
                 resource-limited GP is superior to tree depth limits,
                 but we question this superiority and discuss possible
                 ways of combining the strengths of both approaches, to
                 further improve the results.",
  notes =        "CEC2005 - A joint meeting of the IEEE, the IEE, and
                 the EPS.",

Genetic Programming entries for Sara Silva Ernesto Costa