Identification of Industrial Processes using Genetic Programming

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

  author =       "B. McKay and M. J. Willis and H. G. Hiden and 
                 G. A. Montague and G. W. Barton",
  title =        "Identification of Industrial Processes using Genetic
  booktitle =    "Identification in Engineering Systems",
  year =         "1996",
  editor =       "M. I. Friswell and J. E. Mottershead",
  volume =       "1",
  address =      "Swansea, UK",
  month =        mar,
  publisher =    "The Cromwell Press Ltd",
  note =         "Proceedings of the International Conference, ICIES",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-86076-136-3",
  broken =       "",
  URL =          "",
  size =         "10 pages",
  abstract =     "Complex processes are often modelled using
                 input-output data from experimental tests. Regression
                 and neural network modelling techniques address this
                 problem to some extent and are being increasingly used
                 to develop optimisation or model-based control
                 algorithms. Unfortunately, the latter methods provide
                 no physical insight into the underlying structural
                 relationships inherent within the data. Genetic
                 Programming (GP) is currently finding application in
                 the modelling of processes from experimental data. The
                 nature of GP-based modelling is that solutions are
                 `evolved' from a set of potential solutions in an
                 environment which mimics Darwinian `survival of the
                 fittest'. GP performs symbolic regression, determining
                 both the structure and the complexity of the model
                 during its evolution. In this contribution two examples
                 are used to demonstrate the utility of the GP technique
                 as a process modelling tool. It is concluded that GP
                 techniques may have further applications in the
                 modelling and identification of complex processes from
                 experimental input-output data.",
  notes =        "MSWord postscript not compatible with unix

                 cited by \cite{yeun_2004_tec}",

Genetic Programming entries for Ben McKay Mark J Willis Hugo Hiden Gary A Montague Geoffrey W Barton