New Results on Fuzzy Regression by Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  title =        "New Results on Fuzzy Regression by Using Genetic
  author =       "Wolfgang Golubski",
  editor =       "James A. Foster and Evelyne Lutton and 
                 Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi",
  booktitle =    "Genetic Programming, Proceedings of the 5th European
                 Conference, EuroGP 2002",
  volume =       "2278",
  series =       "LNCS",
  pages =        "308--315",
  publisher =    "Springer-Verlag",
  address =      "Kinsale, Ireland",
  publisher_address = "Berlin",
  month =        "3-5 " # apr,
  year =         "2002",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-43378-3",
  DOI =          "doi:10.1007/3-540-45984-7_30",
  abstract =     "In this paper we continue the work on symbolic fuzzy
                 regression problems. That means that we are interesting
                 in finding a fuzzy function f with best matches k given
                 data pairs (x,y) of fuzzy numbers. We use a genetic
                 programming approach for finding a suitable fuzzy
                 function and will present test results about linear,
                 quadratic and cubic fuzzy functions.",
  notes =        "EuroGP'2002, part of \cite{lutton:2002:GP}",

Genetic Programming entries for Wolfgang Golubski