Adaptive Genetic Programming Applied to New and Existing Simple Regression Problems

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

  author =       "Jeroen Eggermont and Jano I. {van Hemert}",
  title =        "Adaptive Genetic Programming Applied to New and
                 Existing Simple Regression Problems",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2001",
  year =         "2001",
  editor =       "Julian F. Miller and Marco Tomassini and 
                 Pier Luca Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and 
                 William B. Langdon",
  volume =       "2038",
  series =       "LNCS",
  pages =        "23--35",
  address =      "Lake Como, Italy",
  publisher_address = "Berlin",
  month =        "18-20 " # apr,
  organisation = "EvoNET",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, Adaptation,
                 Symbolic Regression, Problem Generator, Program Trees,
                 data mining",
  ISBN =         "3-540-41899-7",
  URL =          "",
  URL =          "",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1007/3-540-45355-5_3",
  size =         "13 pages",
  abstract =     "In this paper we continue our study on adaptive
                 genetic programming. We use Stepwise Adaptation of
                 Weights (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 from literature. Also, we
                 propose a model for randomly generating polynomials
                 which we then use to further test all three GP
  notes =        "EuroGP'2001, part of \cite{miller:2001:gp}",

Genetic Programming entries for Jeroen Eggermont Jano I van Hemert