Empirical modelling of chemical process systems with evolutionary programming

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

  author =       "D. J Greeff and C. Aldrich",
  title =        "Empirical modelling of chemical process systems with
                 evolutionary programming",
  journal =      "Computers \& Chemical Engineering",
  year =         "1998",
  volume =       "22",
  pages =        "995--1005",
  number =       "7-8",
  abstract =     "Through the use of evolutionary computation, empirical
                 models for chemical processes can be evolved that are
                 more cost-effective than models determined by means of
                 classical statistical techniques. These strategies do
                 not require explicit specification of a model
                 structure, but explore candidate models assembled from
                 sets of variables, parameters and simple mathematical
                 operators. The application of the proposed strategies
                 is illustrated by means of three examples, two of which
                 are based on data pertaining to leaching experiments.
                 Since the evolved models were derived from terminal
                 sets containing only the most basic operators, their
                 structures tended to be complicated, making for less
                 easy interpretation, similar to neural networks and
                 other non-parametric models. Nonetheless, the evolved
                 models were either of comparable accuracy or
                 significantly more accurate than those which were
                 previously developed by means of standard least-squares
  owner =        "wlangdon",
  URL =          "http://www.sciencedirect.com/science/article/B6TFT-3TKV02R-F/2/30657596f48ca16571ac48098a948833",
  keywords =     "genetic algorithms, genetic programming, empirical
  DOI =          "doi:10.1016/S0098-1354(97)00271-8",

Genetic Programming entries for D J Greeff C Aldrich