How many neurons?: a genetic programming answer

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Trujillo:2011:GECCOcomp,
  author =       "Leonardo Trujillo and Yuliana Martinez and 
                 Patricia Melin",
  title =        "How many neurons?: a genetic programming answer",
  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, Genetics
                 based machine learning: Poster",
  pages =        "175--176",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001858.2001956",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "The goal of this paper is to derive predictive models
                 that take as input a description of a problem and
                 produce as output an estimate of the optimal number of
                 hidden nodes in an Artificial Neural Network (ANN). We
                 call such computational tools Direct Estimators of
                 Neural Network Topology (DENNT), an use Genetic
                 Programming (GP) to evolve them. The evolved DENNTs
                 take as input statistical and complexity descriptors of
                 the problem data, and output an estimate of the optimal
                 number of hidden neurons.",
  notes =        "Also known as \cite{2001956} Distributed on CD-ROM at
                 GECCO-2011.

                 ACM Order Number 910112.",
}

Genetic Programming entries for Leonardo Trujillo Yuliana Martinez Patricia Melin

Citations