Meta-modeling by symbolic regression and Pareto simulated annealing

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  title =        "Meta-modeling by symbolic regression and Pareto
                 simulated annealing",
  author =       "Erwin Stinstra and Gijs Rennen and Geert Teeuwen",
  year =         "2006",
  institution =  "Tilburg University",
  type =         "Internal report",
  number =       "No. 2006-15",
  address =      "Holland",
  month =        mar,
  bibsource =    "OAI-PMH server at",
  oai =          "",
  rights =       "(c) Universiteit van Tilburg",
  keywords =     "genetic algorithms, genetic programming,
                 approximation, meta-modeling, Pareto simulated
                 annealing, symbolic regression",
  URL =          "",
  URL =          "\&db=wo\&language=eng\&query=193400",
  abstract =     "The subject of this paper is a new approach to
                 Symbolic Regression. Other publications on Symbolic
                 Regression use Genetic Programming. This paper
                 describes an alternative method based on Pareto
                 Simulated Annealing. Our method is based on linear
                 regression for the estimation of constants. Interval
                 arithmetic is applied to ensure the consistency of a
                 model. In order to prevent over-fitting, we merit a
                 model not only on predictions in the data points, but
                 also on the complexity of a model. For the complexity
                 we introduce a new measure. We compare our new method
                 with the Kriging meta-model and against a Symbolic
                 Regression meta-model based on Genetic Programming. We
                 conclude that Pareto Simulated Annealing based Symbolic
                 Regression is very competitive compared to the other
                 meta-model approaches",

Genetic Programming entries for Erwin Stinstra Gijs Rennen Geert Teeuwen