Pareto-coevolutionary genetic programming for problem decomposition in multi-class classification

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{1277058,
  author =       "Peter Lichodzijewski and Malcolm I. Heywood",
  title =        "Pareto-coevolutionary genetic programming for problem
                 decomposition in multi-class classification",
  booktitle =    "GECCO '07: Proceedings of the 9th annual conference on
                 Genetic and evolutionary computation",
  year =         "2007",
  editor =       "Dirk Thierens and Hans-Georg Beyer and 
                 Josh Bongard and Jurgen Branke and John Andrew Clark and 
                 Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and 
                 Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and 
                 Julian F. Miller and Jason Moore and Frank Neumann and 
                 Martin Pelikan and Riccardo Poli and Kumara Sastry and 
                 Kenneth Owen Stanley and Thomas Stutzle and 
                 Richard A Watson and Ingo Wegener",
  volume =       "1",
  isbn13 =       "978-1-59593-697-4",
  pages =        "464--471",
  address =      "London",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p464.pdf",
  DOI =          "doi:10.1145/1276958.1277058",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, USA",
  month =        "7-11 " # jul,
  organisation = "ACM SIGEVO (formerly ISGEC)",
  keywords =     "genetic algorithms, genetic programming, Coevolution,
                 problem decomposition, subset selection, supervised
                 learning, training efficiency",
  abstract =     "A bid-based approach for coevolving Genetic
                 Programming classifiers is presented. The approach
                 Co-evolves a population of learners that decompose the
                 instance space by way of their aggregate bidding
                 behaviour. To reduce computation overhead, a small,
                 relevant, subset of training exemplars is
                 (competitively) coevolved alongside the learners. The
                 approach solves multi-class problems using a single
                 population and is evaluated on three large datasets. It
                 is found to be competitive, especially compared to
                 classifier systems, while significantly reducing the
                 computation overhead associated with training.",
  notes =        "GECCO-2007 A joint meeting of the sixteenth
                 international conference on genetic algorithms
                 (ICGA-2007) and the twelfth annual genetic programming
                 conference (GP-2007).

                 ACM Order Number 910071",
}

Genetic Programming entries for Peter Lichodzijewski Malcolm Heywood

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