Symbolic and numerical regression: experiments and applications

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

  author =       "J. W. Davidson and D. A. Savic and G. A. Walters",
  title =        "Symbolic and numerical regression: experiments and
  booktitle =    "Developments in Soft Computing",
  year =         "2001",
  editor =       "Robert John and Ralph Birkenhead",
  volume =       "9",
  series =       "Advances in Soft Computing",
  pages =        "175--182",
  address =      "De Montfort University, Leicester, UK",
  month =        "29-30 " # jun # " 2000.",
  publisher =    "Physica Verlag",
  publisher_address = "Heidelberg, Germany",
  keywords =     "genetic algorithms, genetic programming,
                 least-squares, rule-based programming, stepwise
                 regression, symbolic regression",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1007/978-3-7908-1829-1_21",
  ISBN =         "3-7908-1361-3",
  abstract =     "This paper describes a new method for creating
                 polynomial regression models. The new method is
                 compared with stepwise regression and symbolic
                 regression using three example problems. The first
                 example is a polynomial equation. The two examples that
                 follow are real-world problems, approximating the
                 Colebrook-White equation and rainfall-runoff

Genetic Programming entries for J W Davidson Dragan Savic Godfrey A Walters