Pareto-Front Exploitation in Symbolic Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

  author =       "Guido Smits and Mark Kotanchek",
  title =        "Pareto-Front Exploitation in Symbolic Regression",
  booktitle =    "Genetic Programming Theory and Practice {II}",
  year =         "2004",
  editor =       "Una-May O'Reilly and Tina Yu and Rick L. Riolo and 
                 Bill Worzel",
  chapter =      "17",
  pages =        "283--299",
  address =      "Ann Arbor",
  month =        "13-15 " # may,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Pareto front,
                 multi-objective optimisation, symbolic regression,
  ISBN =         "0-387-23253-2",
  DOI =          "doi:10.1007/0-387-23254-0_17",
  abstract =     "Symbolic regression via genetic programming
                 (hereafter, referred to simply as symbolic regression)
                 has proved to be a very important tool for industrial
                 empirical modelling (Kotanchek et al., 2003). Two of
                 the primary problems with industrial use of symbolic
                 regression are (1) the relatively large computational
                 demands in comparison with other nonlinear empirical
                 modeling techniques such as neural networks and (2) the
                 difficulty in making the trade-off between expression
                 accuracy and complexity. The latter issue is
                 significant since, in general, we prefer parsimonious
                 (simple) expressions with the expectation that they are
                 more robust with respect to changes over time in the
                 underlying system or extrapolation outside the range of
                 the data used as the reference in evolving the symbolic

                 In this chapter, we present a genetic programming
                 variant, Pareto GP, which exploits the Pareto front to
                 dramatically speed the symbolic regression solution
                 evolution as well as explicitly exploit the
                 complexity-performance trade-off. In addition to the
                 improvement in evolution efficiency, the Pareto front
                 perspective allows the user to choose appropriate
                 models for further analysis or deployment. The Pareto
                 front avoids the need to a priori specify a trade-off
                 between competing objectives (e.g. complexity and
                 performance) by identifying the curve (or surface or
                 hyper-surface) which characterises, for example, the
                 best performance for a given expression complexity.",
  notes =        "part of \cite{oreilly:2004:GPTP2}",

Genetic Programming entries for Guido F Smits Mark Kotanchek