Genetic Programming for Subjective Fitness Function Identification

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Dan Costelloe and Conor Ryan",
  title =        "Genetic Programming for Subjective Fitness Function
  booktitle =    "Genetic Programming 7th European Conference, EuroGP
                 2004, Proceedings",
  year =         "2004",
  editor =       "Maarten Keijzer and Una-May O'Reilly and 
                 Simon M. Lucas and Ernesto Costa and Terence Soule",
  volume =       "3003",
  series =       "LNCS",
  pages =        "259--268",
  address =      "Coimbra, Portugal",
  publisher_address = "Berlin",
  month =        "5-7 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming: Poster",
  ISBN =         "3-540-21346-5",
  URL =          "",
  DOI =          "doi:10.1007/978-3-540-24650-3_24",
  abstract =     "We address modelling fitness functions for Interactive
                 Evolutionary Systems. Such systems are necessarily slow
                 because they need human interaction for the fundamental
                 task of fitness allocation. The research presented here
                 demonstrates that Genetic Programming can be used to
                 learn subjective fitness functions from human subjects,
                 using historical data from an Interactive Evolutionary
                 system for producing pleasing drum patterns. The
                 results indicate that GP is capable of performing
                 symbolic regression even when the number of training
                 cases is substantially less than the number of
  notes =        "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in
                 conjunction with EvoCOP2004 and EvoWorkshops2004",

Genetic Programming entries for Dan Costelloe Conor Ryan