Information Theoretic Indicators of Fitness, Relevant Diversity \& Pairing Potential in Genetic Programming

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@InProceedings{card:2005:CEC,
  author =       "Stuart W. Card and Chilukuri K. Mohan",
  title =        "Information Theoretic Indicators of Fitness, Relevant
                 Diversity \& Pairing Potential in 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 =       "3",
  pages =        "2545--2552",
  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.2005.1555013",
  abstract =     "Commonly used fitness measures, such as mean squared
                 error, often fail to reward individuals whose presence
                 in the population is necessary to explain substantial
                 portions of the data variance. Diversity indicators are
                 often arbitrary, may reflect diversity irrelevant to
                 solving the problem, and are incommensurate with
                 fitness measures. By contrast, information theoretic
                 functionals are computable general indicators of
                 fitness and diversity without these typical failings.
                 We propose normalised mutual information, redundancy
                 and synergy measures for genetic programming. We also
                 propose selection for recombination and survival by
                 {"}pairing potential{"} and {"}pair potential{"}
                 estimation, and offer numerical examples as empirical
                 support for theoretical claims.",
  notes =        "CEC2005 - A joint meeting of the IEEE, the IEE, and
                 the EPS.

                 Syracuse University 7417 S. Main St. P.O. Box 61
                 Newport, NY 13416",
}

Genetic Programming entries for Stu Card Chilukuri K Mohan

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