Nonlinear continuum regression: an evolutionary approach

Created by W.Langdon from gp-bibliography.bib Revision:1.2715

@Article{Mckay:2000:TIMC,
  author =       "Ben McKay and Mark Willis and Dominic Searson and 
                 Gary Montague",
  title =        "Nonlinear continuum regression: an evolutionary
                 approach",
  journal =      "Transactions of the Institute of Measurement and
                 Control",
  year =         "2000",
  volume =       "22",
  number =       "2",
  pages =        "125--140",
  email =        "mark.willis@ncl.ac.uk",
  keywords =     "genetic algorithms, genetic programming, continuum
                 regression, process modelling, co-evolution",
  doi =          "doi:10.1177/014233120002200202",
  abstract =     "genetic programming is combined with continuum
                 regression to produce two novel non-linear continuum
                 regression algorithms. The first is a sequential
                 algorithm while the second adopts a team-based
                 strategy. Having discussed continuum regression, the
                 modifications required to extend the algorithm for
                 non-linear modelling are outlined. The results of two
                 case studies are then presented: the development of an
                 inferential model of a food extrusion process and an
                 input-output model of an industrial bioreactor. The
                 superior performance of the sequential continuum
                 regression algorithm, as compared to a similar
                 sequential nonlinear partial least squares algorithm,
                 is demonstrated. These applications clearly demonstrate
                 that the team-based continuum regression strategy
                 significantly outperforms both sequential approaches.",
}

Genetic Programming entries for Ben McKay Mark J Willis Dominic Patrick Searson Gary A Montague