Early stopping criteria to counteract overfitting in genetic programming

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

@InProceedings{Tuite:2011:GECCOcomp,
  author =       "Cliodhna Tuite and Alexandros Agapitos and 
                 Michael O'Neill and Anthony Brabazon",
  title =        "Early stopping criteria to counteract overfitting 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",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution: Poster",
  pages =        "203--204",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  doi =          "doi:10.1145/2001858.2001971",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Early stopping typically stops training the first time
                 validation fitness disimproves. This may not be the
                 best strategy given that validation fitness can
                 subsequently increase or decrease. We examine the
                 effects of stopping subsequent to the first
                 disimprovement in validation fitness, on symbolic
                 regression problems. Stopping points are determined
                 using criteria which measure generalisation loss and
                 training progress. Results suggest that these criteria
                 can improve the generalistion ability of symbolic
                 regression functions evolved using Grammar-based GP.",
  notes =        "Also known as \cite{2001971} Distributed on CD-ROM at
                 GECCO-2011.

                 ACM Order Number 910112.",
}

Genetic Programming entries for Cliodhna Tuite Alexandros Agapitos Michael O'Neill Anthony Brabazon