Co-evolution of non-linear PLS model components

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@Article{Searson:2007:JC,
  author =       "Dominic Searson and Mark Willis and Gary Montague",
  title =        "Co-evolution of non-linear PLS model components",
  journal =      "Journal of Chemometrics",
  year =         "2007",
  volume =       "21",
  number =       "12",
  pages =        "592--603",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, partial least
                 squares, symbolic regression, evolutionary computation,
                 co-operative co-evolution",
  ISSN =         "1099-128X",
  ISSN =         "0886-9383",
  DOI =          "doi:10.1002/cem.1084",
  abstract =     "The issue of outer model weight updating is important
                 in extending partial least squares (PLS) regression to
                 modelling data that shows significant non-linearity.
                 This paper presents a novel co-evolutionary component
                 approach to the weight updating problem. Specification
                 of the non-linear PLS model is achieved using an
                 evolutionary computational (EC) method that can
                 co-evolve all non-linear inner models and all input
                 projection weights simultaneously. In this method,
                 modular symbolic non-linear equations are used to
                 represent the inner models and binary sequences are
                 used to represent the projection weights. The approach
                 is flexible, and other representations could be
                 employed within the same co-evolutionary framework. The
                 potential of these methods is illustrated using a
                 simulated pH neutralisation process data set exhibiting
                 significant non-linearity. It is demonstrated that the
                 co-evolutionary component architecture can produce
                 results which are competitive with non-linear neural
                 network-based PLS algorithms that use iterative
                 projection weight updating. In addition, a data
                 sampling method for mitigating overfitting to the
                 training data is described",
}

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

Citations