A comparison of two Genetic Programming Algorithms Applied to Chemical Process Systems Modelling

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@TechReport{hinchcliffe:1996:c2GPcpsm,
  author =       "Mark Hinchliffe and Mark Willis and Hugo Hiden and 
                 Ming Tham",
  title =        "A comparison of two Genetic Programming Algorithms
                 Applied to Chemical Process Systems Modelling",
  institution =  "Chemical Engineering, Newcastle University",
  year =         "1996",
  address =      "UK",
  note =         "Extended Abstract, submitted to: ICANNGA '97, Norwick,
                 UK",
  keywords =     "genetic algorithms, genetic programming",
  broken =       "http://lorien.ncl.ac.uk/sorg/paper10a.ps",
  abstract =     "Previous work by McKay et al (1996a,b,c) has shown
                 that the Genetic programming (GP) methodology can be
                 successfully applied to the development of non-linear
                 steady state models of industrial chemical processes.
                 Although a GP algorithm can identify the relevant input
                 variables and evolve parsimonious system
                 representations, the resulting model structures tend to
                 contain little or no information relating to the
                 mechanisms of the process itself. In this respect, the
                 performance of the GP methodology is comparable to that
                 of other black-box modelling techniques such as neural
                 networks. Chemical process systems are often extremely
                 complex and non-linear in nature. Phenomenological
                 models are time consuming to develop and can be subject
                 to inaccuracies caused by any simplifying assumptions
                 made. Consequently, mechanistic models are costly to
                 construct; an aspect which would make an automated
                 procedure highly desirable. Phenomenological models are
                 usually derived by applying the laws of conservation of
                 mass, energy and momentum to the system. An examination
                 of a number of steady-state mechanistic models shows
                 that they tend to be made up of distinct sub-groups
                 which, when added together, give the overall model
                 structure. In the search for an automatic model
                 generating algorithm, it would be extremely useful if
                 the GP methodology could be used to identify these
                 sub-groups. This could potentially enhance the GP
                 algorithm's ability to evolve accurate chemical process
                 models and also help to reveal hidden process
                 knowledge. To achieve this goal, the standard GP
                 algorithm used by McKay et al (1996a) was modified to
                 accommodate the multiple gene model structure. The
                 multiple gene structure was introduced by Altenberg
                 (1994) in an attempt to enhance the learning
                 capabilities of GA and GP algorithms. The work was
                 inspired by the observation that, in nature, genetic
                 information is stored on more than one gene. To
                 demonstrate the feasibility of this new approach, real
                 world examples are used to compare the performance of
                 the algorithm with that of the standard GP algorithm.",
  notes =        "MSword postscript not camptible with unix",
  size =         "7 pages",
}

Genetic Programming entries for Mark P Hinchliffe Mark J Willis Hugo Hiden Ming T Tham

Citations