Boolean Genetic Programming for Promoter Recognition in Eukaryotes

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Singer X. J. Wang and Peter Lichodzijewski",
  title =        "Boolean Genetic Programming for Promoter Recognition
                 in Eukaryotes",
  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 =        "683--690",
  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",
  URL =          "",
  DOI =          "doi:10.1109/CEC.2005.1554749",
  abstract =     "Fixed-length Genetic Programming is applied to the
                 problem of promoter identification in eukaryotes. The
                 goal is to generate solutions that can be easily
                 interpreted and compared with known promoter
                 characteristics. Using a boolean function set applied
                 to boolean registers, inputs, and constant values, the
                 approach builds a logical expression whose value gives
                 the classification decision. Evaluated on a dataset of
                 human promoters and non-promoters from coding regions,
                 the approach is found to generate concise solutions
                 that yield good specificity but poor sensitivity.
                 Analysis of the programs that are generated indicates
                 that a well-known, biologically significant,
                 characteristic of promoter regions is successfully
                 identified. Suggested future work involves implementing
                 the system using fuzzy logic.",
  notes =        "CEC2005 - A joint meeting of the IEEE, the IEE, and
                 the EPS.",

Genetic Programming entries for Singer X J Wang Peter Lichodzijewski