Benchmarking coevolutionary teaming under classification problems with large attribute spaces

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@InProceedings{DBLP:conf/gecco/DoucetteLH09,
  author =       "John Doucette and Peter Lichodzijewski and 
                 Malcolm I. Heywood",
  title =        "Benchmarking coevolutionary teaming under
                 classification problems with large attribute spaces",
  booktitle =    "GECCO '09: Proceedings of the 11th Annual conference
                 on Genetic and evolutionary computation",
  year =         "2009",
  editor =       "Guenther Raidl and Franz Rothlauf and 
                 Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and 
                 Mauro Birattari and Clare Bates Congdon and 
                 Martin Middendorf and Christian Blum and Carlos Cotta and 
                 Peter Bosman and Joern Grahl and Joshua Knowles and 
                 David Corne and Hans-Georg Beyer and Ken Stanley and 
                 Julian F. Miller and Jano {van Hemert} and 
                 Tom Lenaerts and Marc Ebner and Jaume Bacardit and 
                 Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and 
                 Thomas Jansen and Riccardo Poli and Enrique Alba",
  pages =        "1901--1902",
  address =      "Montreal",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "8-12 " # jul,
  organisation = "SigEvo",
  keywords =     "genetic algorithms, genetic programming, Poster",
  isbn13 =       "978-1-60558-325-9",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  doi =          "doi:10.1145/1569901.1570226",
  abstract =     "Benchmarking of a team based model of Genetic
                 Programming demonstrates that the naturally embedded
                 style of feature selection is usefully extended by the
                 teaming metaphor to provide solutions in terms of
                 exceptionally low attribute counts. To take this
                 concept to its logical conclusion the teaming model
                 must be able to build teams with a non-overlapping
                 behavioral trait, from a single population. The
                 Symbiotic Bid-Based (SBB) algorithm is demonstrated to
                 fit this purpose under an evaluation using data sets
                 with 650 to 5,000 attributes. The resulting solutions
                 are one to two orders simpler than solutions identified
                 under the alternative embedded paradigms of C4.5 and
                 MaxEnt.",
  notes =        "GECCO-2009 A joint meeting of the eighteenth
                 international conference on genetic algorithms
                 (ICGA-2009) and the fourteenth annual genetic
                 programming conference (GP-2009).

                 ACM Order Number 910092.",
}

Genetic Programming entries for John Doucette Peter Lichodzijewski Malcolm Heywood