A Statistical Comparison of Grammatical Evolution Strategies in the Domain of Human Genetics

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

@InProceedings{white:2005:CEC,
  author =       "Bill C. White and Joshua C. Gilbert and 
                 David M. Reif and Jason H. Moore",
  title =        "A Statistical Comparison of Grammatical Evolution
                 Strategies in the Domain of Human Genetics",
  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 =        "491--497",
  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, PSO,
                 grammatical evolution",
  ISBN =         "0-7803-9363-5",
  DOI =          "doi:10.1109/CEC.2005.1554723",
  abstract =     "Detecting and characterising genetic predictors of
                 human disease susceptibility is an important goal in
                 human genetics. New chip-based technologies are
                 available that facilitate the measurement of thousands
                 of DNA sequence variations across the human genome.
                 Biologically-inspired stochastic search algorithms are
                 expected to play an important role in the analysis of
                 these high-dimensional datasets. We simulated datasets
                 with up to 6000 attributes using two different genetic
                 models and statistically compared the performance of
                 grammatical evolution, grammatical swarm, and random
                 search for building symbolic discriminant functions. We
                 found no statistical difference among search algorithms
                 within this specific domain.",
  notes =        "CEC2005 - A joint meeting of the IEEE, the IEE, and
                 the EPS.

                 also appears at pages 676-682",
}

Genetic Programming entries for Bill C White Joshua C Gilbert David M Reif Jason H Moore

Citations