Optimization of grammatical evolution decision trees

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

  author =       "Kristopher Hoover and Rachel Marceau and 
                 Tyndall Harris and Nicholas Hardison and David Reif and 
                 Alison Motsinger-Reif",
  title =        "Optimization of grammatical evolution decision trees",
  booktitle =    "GECCO '11: Proceedings of the 13th annual conference
                 companion on Genetic and evolutionary computation",
  year =         "2011",
  editor =       "Natalio Krasnogor and Pier Luca Lanzi and 
                 Andries Engelbrecht and David Pelta and Carlos Gershenson and 
                 Giovanni Squillero and Alex Freitas and 
                 Marylyn Ritchie and Mike Preuss and Christian Gagne and 
                 Yew Soon Ong and Guenther Raidl and Marcus Gallager and 
                 Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and 
                 Nikolaus Hansen and Silja Meyer-Nieberg and 
                 Jim Smith and Gus Eiben and Ester Bernado-Mansilla and 
                 Will Browne and Lee Spector and Tina Yu and Jeff Clune and 
                 Greg Hornby and Man-Leung Wong and Pierre Collet and 
                 Steve Gustafson and Jean-Paul Watson and 
                 Moshe Sipper and Simon Poulding and Gabriela Ochoa and 
                 Marc Schoenauer and Carsten Witt and Anne Auger",
  isbn13 =       "978-1-4503-0690-4",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, Bioinformatics, computational, systems, and
                 synthetic biology: Poster",
  pages =        "35--36",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001858.2001879",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "The detection of gene-gene and gene-environment
                 interactions in genetic association studies presents a
                 difficult computational and statistical challenge,
                 especially as advances in genotyping technology have
                 rapidly expanded the number of potential genetic
                 predictors in such studies. The scale of these studies
                 makes exhaustive search approaches infeasible,
                 inspiring the application of evolutionary computation
                 algorithms to perform variable selection and build
                 classification models. Recently, an application of
                 grammatical evolution to evolve decision trees (GEDT)
                 has been introduced for detecting interaction models.
                 Initial results were promising, but relied on arbitrary
                 parameter choices for the evolutionary process. In the
                 current study, we present the results of a parameter
                 sweep evaluating the power of GEDT and show that
                 improved parameter choices improves the performance of
                 the method. The results of these experiments are
                 important for the continued optimisation, evaluation,
                 and comparison of this and related methods, and for
                 proper application in real data.",
  notes =        "Also known as \cite{2001879} Distributed on CD-ROM at

                 ACM Order Number 910112.",

Genetic Programming entries for Kristopher Hoover Rachel Marceau Tyndall Harris Nicholas E Hardison David M Reif Alison A Motsinger