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

by

Hinchliffe, M., Willis, M., Hiden, H. and Tham, M.

Literature search on Evolutionary ComputationBBase ©1999-2013, Rasmus K. Ursem
     Home · Search · Adv. search · Authors · Login · Add entries   Webmaster
Note to authors: Please submit your bibliography and contact information - online papers are more frequently cited.

Info: 1996
Keywords:genetic algorithms, genetic programming
Abstract:
Previous work by Mckay et al (1996a,b,c) has shown that the Genetic programming (GP) methodology [GP] can be successfully applied to the development of non-linear steady state models [SS] 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 [NN] 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 utilised 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
URL(s):Postscript
(G)zipped postscript

Review item:

Mark as doublet (will be reviewed)

Print entry



BibTex:
@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",
  URL =          "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 utilised 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",
}