Systems Modelling Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Mark Willis and Hugo Hiden and Mark Hinchliffe and 
                 Ben McKay and Geoffrey W. Barton",
  title =        "Systems Modelling Using Genetic Programming",
  journal =      "Computers in Chemical Engineering",
  year =         "1997",
  volume =       "21",
  pages =        "S1161--S1166",
  note =         "Supplemental",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  DOI =          "doi:10.1016/S0098-1354(97)87659-4",
  size =         "5 pages",
  abstract =     "In this contribution, a Genetic Programming (GP)
                 algorithm is used to develop empirical models of
                 chemical process systems. GP performs symbolic
                 regression, determining both the structure and the
                 complexity of a model. Initially, steady-state model
                 development using a GP algorithm is considered, next
                 the methodology is extended to the development of
                 dynamic input-output models. The usefulness of the
                 technique is demonstrated by the development of
                 inferential estimation models for two typical
                 processes: a vacuum distillation column and a twin
                 screw cooking extruder.",
  notes =        "GP empirical model of vacuum distillation column and a
                 twin screw extruder for processing corn flour.
                 Comparison of artifical neural network and GP",

Genetic Programming entries for Mark J Willis Hugo Hiden Mark P Hinchliffe Ben McKay Geoffrey W Barton