Knowledge Discovery through Symbolic Regression with HeuristicLab

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

  author =       "Gabriel Kronberger and Stefan Wagner and 
                 Michael Kommenda and Andreas Beham and 
                 Andreas Scheibenpflug and Michael Affenzeller",
  title =        "Knowledge Discovery through Symbolic Regression with
  booktitle =    "Conference booklet ECML-PKDD 2012",
  year =         "2012",
  editor =       "Bettina Berendt and Myra Spiliopoulou",
  volume =       "7524",
  series =       "Lecture Notes in Computer Science",
  pages =        "824--827",
  address =      "Bristol UK",
  month =        "24-28 " # sep,
  publisher =    "Springer",
  note =         "Demo Spotlights",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-33485-6",
  DOI =          "doi:10.1007/978-3-642-33486-3_56",
  size =         "4 pages",
  abstract =     "This contribution describes how symbolic regression
                 can be used for knowledge discovery with the
                 open-source software HeuristicLab. HeuristicLab
                 includes a large set of algorithms and problems for
                 combinatorial optimisation and for regression and
                 classification, including symbolic regression with
                 genetic programming. It provides a rich GUI to analyse
                 and compare algorithms and identified models. This
                 contribution mainly focuses on specific aspects of
                 symbolic regression that are unique to HeuristicLab, in
                 particular, the identification of relevant variables
                 and model simplification.",
  notes =        "LNCS gives in Machine Learning and Knowledge Discovery
                 in Databases editor={Flach, PeterA. and Bie, Tijl and


Genetic Programming entries for Gabriel Kronberger Stefan Wagner Michael Kommenda Andreas Beham Andreas Scheibenpflug Michael Affenzeller