Dynamic Modelling Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{hinchliffe:thesis,
  author =       "Mark P. Hinchliffe",
  title =        "Dynamic Modelling Using Genetic Programming",
  school =       "School of Chemical Engineering and Advanced Materials,
                 University of Newcastle upon Tyne",
  year =         "2001",
  address =      "UK",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming, MOGA, MOGP,
                 SOGP",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/hinchliffe:Thesis.pdf",
  broken =       "http://www.ncl.ac.uk/ceam/postgrad/pg-theses.htm",
  URL =          "http://ethos.bl.uk/OrderDetails.do?did=5&uin=uk.bl.ethos.391407",
  size =         "205 pages",
  abstract =     "Genetic programming (GP) is an evolutionary algorithm
                 that attempts to evolve solutions to a problem by using
                 concepts taken from the naturally occurring
                 evolutionary process. This thesis introduces the
                 concepts of GP model development by applying the
                 technique to steady-state model evolution. A variation
                 of the algorithm known as the multiple basis function
                 GP (MBF-GP) algorithm is described and its performance
                 compared with the standard algorithm. Results show that
                 the MBF-GP algorithm requires significantly less
                 computational effort to evolve models of comparable
                 accuracy to the standard algorithm. The steady-state
                 algorithm is then modified to enable the evolution of
                 dynamic process models. Three case studies are used to
                 demonstrate algorithm performance and show how the
                 MBF-GP algorithm produces performance benefits similar
                 to those observed in the steady-state modelling work. A
                 comparison with neural networks reveals that GP is able
                 to match the accuracy of the network predictions but is
                 more expensive computationally. However, a significant
                 advantage of the GP algorithm is that it can
                 automatically evolve the time history of model terms
                 required to account for process characteristics such as
                 the system time delay.

                 The model development process is not simply a case of
                 reducing the error between the predicted and actual
                 process output. The parallel nature of GP means that it
                 is ideally suited to solving multi-objective problems.
                 The MBF-GP algorithm is modified to incorporate a
                 Pareto based ranking scheme that allows models to be
                 compared using multiple performance criteria. The
                 ranking scheme allows preference information in the
                 form of goals and priorities to be specified in order
                 to guide the search towards the desired region of the
                 search space. Two case studies are used to demonstrate
                 the performance of this technique. The first example
                 uses the multi-objective algorithm to improve the
                 parsimony of the evolved model structures. The second
                 example demonstrates how a set residual correlation
                 tests can be combined and used as an additional
                 performance measure. In each case, the multi-objective
                 algorithm performs significantly better than the single
                 objective version. In addition, the inclusion of
                 preference information overcomes some of the
                 difficulties associated with conventional Pareto
                 ranking and produces a greater number of acceptable
                 solutions.",
  notes =        "{"}the results do not provide sufficient evidence to
                 suggest that GP will become as widely used as neural
                 network modelling techniques.{"} page
                 160.

                 uk.bl.ethos.391407",
}

Genetic Programming entries for Mark P Hinchliffe

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