Non-linear PLS using genetic programming

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

@PhdThesis{searson:thesis,
  author =       "Dominic Patrick Searson",
  title =        "Non-linear PLS using genetic programming",
  school =       "University of Newcastle upon Tyne",
  year =         "2002",
  email =        "d.p.searson@ncl.ac.uk",
  keywords =     "genetic algorithms, genetic programming, multivariate
                 models, multigene, co-evolution",
  URL =          "http://www.staff.ncl.ac.uk/d.p.searson/docs/SearsonGP_PLS.pdf",
  URL =          "http://ethos.bl.uk/OrderDetails.do?did=1&uin=uk.bl.ethos.246644",
  abstract =     "The economic and safe operation of modern industrial
                 process plants usually requires that accurate models of
                 the processes are available. Unfortunately, detailed
                 mathematical models of industrial process systems are
                 often time consuming and expensive to develop.
                 Consequently, the use of data based models is often the
                 only practical alternative. The need for effective
                 methods to build accurate data based models with a
                 minimum of specialist knowledge has given impetus to
                 the research of automatic model development methods.
                 One method, genetic programming (GP), which is an
                 evolutionary computational technique for automatically
                 learning how to solve problems, has previously been
                 identified as a candidate for automatic non-linear
                 model development. GP has also been combined with a
                 multivariate statistical regression method called PLS
                 (partial least squares) in order to improve its
                 performance (GP-PLS). One version of this method,
                 called GP_NPLS2, was found to give good performance but
                 at a computational expense deemed too high for use as a
                 modelling tool.

                 In this thesis, the GP-PLS framework is developed
                 further. A novel architecture, called team based
                 GP-PLS, is proposed. This method evolves teams of
                 co-operating sub-models in parallel in an attempt to
                 improve modelling performance without incurring
                 significant additional computational expense. The
                 performance of the team based method is compared with
                 the original formulations of GP-PLS on steady state
                 data sets from three synthetic test systems.
                 Subsequently, a number of other modifications are made
                 to the GP-PLS algorithms. These include the use of a
                 multiple gene sub-model representation and a novel
                 training method used to improve the ability of the
                 evolved models to generalise to unseen data. Finally,
                 an extended team method that encodes certain PLS
                 parameters (the input projection weights) as binary
                 team members is presented. The extended team method
                 allows the optimisation of the sub-models and the
                 projection weights simultaneously without recourse to
                 computationally expensive iterative methods.",
  notes =        "uk.bl.ethos.246644",
}

Genetic Programming entries for Dominic Patrick Searson

Citations