Generalisation in Genetic Programming

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

@InProceedings{langdon:2011:gecco,
  author =       "William B. Langdon",
  title =        "Generalisation in Genetic Programming",
  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",
  pages =        "205",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming: Poster, AI,
                 Problem Solving, Control Methods, and Search, Heuristic
                 methods, Theory, 11-Mux, GPGPU, GPU, bloat, over
                 fitting",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2011_gecco.pdf",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2011_gecco.ps.gz",
  doi =          "doi:10.1145/2001858.2001972",
  size =         "1 page",
  abstract =     "The cost of optimisation can be reduced by evaluating
                 the value of candidate designs on only a fraction of
                 all possible fitness cases. We show how genetic
                 programming (GP) can avoid overfitting and evolve
                 general solutions from test suites as small as just one
                 dynamic training case, thereby greatly reducing search
                 effort.",
  notes =        "Fuller version in \cite{langdon:2011:geccoRN} Also
                 known as \cite{2001972} Distributed on CD-ROM at
                 GECCO-2011.

                 ACM Order Number 910112.",
}

Genetic Programming entries for William B Langdon