Improving Symbolic Regression with Interval Arithmetic and Linear Scaling

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Maarten Keijzer",
  title =        "Improving Symbolic Regression with Interval Arithmetic
                 and Linear Scaling",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2003",
  year =         "2003",
  editor =       "Conor Ryan and Terence Soule and Maarten Keijzer and 
                 Edward Tsang and Riccardo Poli and Ernesto Costa",
  volume =       "2610",
  series =       "LNCS",
  pages =        "70--82",
  address =      "Essex",
  publisher_address = "Berlin",
  month =        "14-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-00971-X",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1007/3-540-36599-0_7",
  abstract =     "The use of protected operators and squared error
                 measures are standard approaches in symbolic
                 regression. It will be shown that two relatively minor
                 modifications of a symbolic regression system can
                 result in greatly improved predictive performance and
                 reliability of the induced expressions. To achieve
                 this, interval arithmetic and linear scaling are used.
                 An experimental section demonstrates the improvements
                 on 15 symbolic regression problems.",
  notes =        "Cited by \cite{Ni:2012:ieeeTEC}.

                 EuroGP'2003 held in conjunction with EvoWorkshops

Genetic Programming entries for Maarten Keijzer