Genetic Programming with Boosting for Ambiguities in Regression Problems

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Gregory Paris and Denis Robilliard and Cyril Fonlupt",
  title =        "Genetic Programming with Boosting for Ambiguities in
                 Regression Problems",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2003",
  year =         "2003",
  editor =       "Conor Ryan and Terence Soule and Maarten Keijzer and 
                 Edward Tsang and Riccardo Poli and Ernesto Costa",
  volume =       "2610",
  series =       "LNCS",
  pages =        "183--193",
  address =      "Essex",
  publisher_address = "Berlin",
  month =        "14-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-00971-X",
  URL =          "",
  DOI =          "doi:10.1007/3-540-36599-0_17",
  abstract =     "Facing ambiguities in regression problems is a
                 challenge. There exists many powerful evolutionary
                 schemes to deal with regression, however, these
                 techniques do not usually take into account ambiguities
                 (i.e. the existence of 2 or more solutions for some or
                 all points in the domain). Nonetheless ambiguities are
                 present in some real world inverse problems, and it is
                 interesting in such cases to provide the user with a
                 choice of possible solutions. We propose in this
                 article an approach based on boosted genetic
                 programming in order to propose several solutions when
                 ambiguities are detected.",
  notes =        "EuroGP'2003 held in conjunction with EvoWorkshops

Genetic Programming entries for Gregory Paris Denis Robilliard Cyril Fonlupt