Automated Discovery of Polynomials by Inductive Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Nikolay Nikolaev and Hitoshi Iba",
  year =         "1999",
  title =        "Automated Discovery of Polynomials by Inductive
                 Genetic Programming",
  booktitle =    "Proceedings of the 3rd European Conference on
                 Principles of Data Mining and Knowledge Discovery
  editor =       "Jan M. Zytkow and Jan Rauch",
  volume =       "1704",
  series =       "LNAI",
  publisher =    "Springer",
  pages =        "456--461",
  month =        sep # "~15--18",
  address =      "Prague, Czech Republic",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-66490-4",
  ISSN =         "0302-9743",
  DOI =          "doi:10.1007/b72280",
  DOI =          "doi:10.1007/978-3-540-48247-5_58",
  abstract =     "This paper presents an approach to automated discovery
                 of high-order multivariate polynomials by inductive
                 Genetic Programming (iGP). Evolutionary search is used
                 for learning polynomials represented as non-linear
                 multivariate trees. Optimal search performance is
                 pursued with balancing the statistical bias and the
                 variance of iGP. We reduce the bias by extending the
                 set of basis polynomials for better agreement with the
                 examples. Possible overfitting due to the reduced bias
                 is conteracted by a variance component, implemented as
                 a regularizing factor of the error in an MDL fitness
                 function. Experimental results demonstrate that
                 regularized iGP discovers accurate, parsimonious, and
                 predictive polynomials when trained on practical data
                 mining tasks.",
  notes =        "Online Date: June 2004",

Genetic Programming entries for Nikolay Nikolaev Hitoshi Iba