Evolving Input-Output Models of Chemical Process Systems Using Genetic Programming

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

  author =       "Ben McKay and Justin Elsey and Mark J. Willis and 
                 Geoffrey W. Barton",
  title =        "Evolving Input-Output Models of Chemical Process
                 Systems Using Genetic Programming",
  booktitle =    "IFAC '96",
  year =         "1996",
  volume =       "1",
  address =      "San-Fransisco",
  keywords =     "genetic algorithms, genetic programming",
  broken =       "http://lorien.ncl.ac.uk/sorg/paper3.ps",
  size =         "7 pages",
  abstract =     "Complex processes are often modelled using
                 input-output data from experimental tests. Regression
                 and neural network modelling techniques are commonly
                 used for this purpose. Unfortunately, these methods
                 provide minimal structural insight into process
                 characteristics. In this contribution, we propose the
                 use of Genetic Programming (GP) as a method for
                 developing input-output process models from
                 experimental data. GP performs symbolic regression,
                 determining both the structure and the complexity of
                 the model during its evolution. This has the advantage
                 that no a priori modelling assumptions have to be made.
                 Moreover, the technique can discriminate between
                 relevant and irrelevant process inputs, yielding
                 parsimonious model structures that accurately represent
                 process characteristics. Two examples are used to
                 demonstrate the utility of the GP technique as a
                 process modelling tool.",
  notes =        "MSWord postscript not compatible with unix",

Genetic Programming entries for Ben McKay Justin Elsey Mark J Willis Geoffrey W Barton