- 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, ParetoGP",
- 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
regression.
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}",
