Modelling Chemical Process Systems Using a Multi-Gene Genetic Programming Algorithm   [GP]

by

Hinchliffe, M., Hiden, H., McKay, B., Willis, M., Tham, M. and Barton, G.

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Info: Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996 (Conference proceedings), 1996, p. 56-65
Keywords:genetic algorithms, genetic programming
Abstract:
In this contribution a multi-gene Genetic Programming (Gp) Algorithm [GP] is used to evolve input output models of chemical process systems. Three case studies are used to demonstrate the performance of the method when compared to a standard GP algorithm. A statistical analysis procedure is used to aid in the assessment of the results and suggest the number [TNO] of independent runs required to obtain a successful result. It is concluded that the multi-gene algorithm provides superior performance, as partitioning the problem into sub-groups incorporates basic heuristic knowledge of the search space. [SS]
Notes:
GP-96LB MSword .ps file not compatible with unix The email address for the bookstore for mail orders is mailorder@bookstore.stanford.edu Phone no 415-329-1217 or 800-533-2670
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BibTex:
@InProceedings{hinchliffe:1996:mcpsm-g,
  author =       "Mark Hinchliffe and Hugo Hiden and Ben McKay and Mark
                 Willis and Ming Tham and Geoffery Barton",
  title =        "Modelling Chemical Process Systems Using a Multi-Gene
                 Genetic Programming Algorithm",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1996
                 Conference Stanford University July 28-31, 1996",
  year =         "1996",
  editor =       "John R. Koza",
  pages =        "56--65",
  address =      "Stanford University, CA, USA",
  publisher_address = "Stanford University, Stanford, California
                 94305-3079, USA",
  month =        "28--31 " # jul,
  publisher =    "Stanford Bookstore",
  ISBN =         "0-18-201031-7",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://lorien.ncl.ac.uk/sorg/paper7.ps",
  abstract =     "In this contribution a multi-gene Genetic Programming
                 (Gp) Algorithm is used to evolve input output models of
                 chemical process systems. Three case studies are used
                 to demonstrate the performance of the method when
                 compared to a standard GP algorithm. A statistical
                 analysis procedure is used to aid in the assessment of
                 the results and suggest the number of independent runs
                 required to obtain a successful result. It is concluded
                 that the multi-gene algorithm provides superior
                 performance, as partitioning the problem into
                 sub-groups incorporates basic heuristic knowledge of
                 the search space.",
  notes =        "GP-96LB MSword .ps file not compatible with unix The
                 email address for the bookstore for mail orders is
                 mailorder@bookstore.stanford.edu Phone no 415-329-1217
                 or 800-533-2670",
}