Stepwise Adaptation of Weights for Symbolic Regression with Genetic Programming

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

  author =       "J. Eggermont and J. I. {van Hemert}",
  title =        "Stepwise Adaptation of Weights for Symbolic Regression
                 with Genetic Programming",
  booktitle =    "Proceedings of the Twelveth Belgium/Netherlands
                 Conference on Artificial Intelligence (BNAIC'00)",
  year =         "2000",
  editor =       "Antal {van den Bosch} and Hans Weigand",
  pages =        "259--266",
  address =      "De Efteling, Kaatsheuvel, Holland",
  month =        "1-2 " # nov,
  organisation = "BNVKI, Dutch and the Belgian AI Association",
  keywords =     "genetic algorithms, genetic programming, data mining",
  URL =          "",
  URL =          "",
  URL =          "",
  URL =          "",
  abstract =     "In this paper we continue study on the Stepwise
                 Adaptation of Weights (SAW) technique. Previous studies
                 on constraint satisfaction and data classification have
                 indicated that SAW is a promising technique to boost
                 the performance of evolutionary algorithms. Here we use
                 SAW to boost performance of a genetic programming
                 algorithm on simple symbolic regression problems. We
                 measure the performance of a standard GP and two
                 variants of SAW extensions on two different symbolic
                 regression problems.",

Genetic Programming entries for Jeroen Eggermont Jano I van Hemert