Dynamic systems modelling using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.2458

@Article{Hinchliffe:2003:CCE,
  author =       "Mark P. Hinchliffe and Mark J. Willis",
  title =        "Dynamic systems modelling using genetic programming",
  journal =      "Computers \& Chemical Engineering",
  year =         "2003",
  volume =       "27",
  pages =        "1841--1854",
  number =       "12",
  owner =        "wlangdon",
  keywords =     "genetic algorithms, genetic programming, Neural
                 networks, Dynamic modelling, Multi-objective",
  ISSN =         "0098-1354",
  URL =          "http://www.sciencedirect.com/science/article/B6TFT-49MDYGW-2/2/742bcc7f22240c7a0381027aa5ff7e73",
  doi =          "doi:10.1016/j.compchemeng.2003.06.001",
  abstract =     "In this contribution genetic programming (GP) is used
                 to evolve dynamic process models. An innovative feature
                 of the GP algorithm is its ability to automatically
                 discover the appropriate time history of model terms
                 required to build an accurate model. Two case studies
                 are used to compare the performance of the GP algorithm
                 with that of filter-based neural networks (FBNNs).
                 Although the models generated using GP have comparable
                 prediction performance to the FBNN models, a
                 disadvantage is that they required greater
                 computational effort to develop. However, we show that
                 a major benefit of the GP approach is that additional
                 model performance criteria can be included during the
                 model development process. The parallel nature of GP
                 means that it can evolve a set of candidate solutions
                 with varying levels of performance in each objective.
                 Although any combination of model performance criteria
                 could be used as objectives within a multi-objective GP
                 (MOGP) framework, the correlation tests outlined by
                 Billings and Voon (Int. J. Control 44 (1986) 235) were
                 used in this work.",
}

Genetic Programming entries for Mark P Hinchliffe Mark J Willis