Model selection methodology in supervised learning with evolutionary computation

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  author =       "J. J. Rowland",
  title =        "Model selection methodology in supervised learning
                 with evolutionary computation",
  journal =      "Biosystems",
  year =         "2003",
  volume =       "72",
  number =       "1-2",
  pages =        "187--196",
  month =        nov,
  keywords =     "genetic algorithms, genetic programming, Gene
                 expression, Generalization, Model selection,
  URL =          "",
  DOI =          "doi:10.1016/S0303-2647(03)00143-6",
  abstract =     "The expressive power, powerful search capability, and
                 the explicit nature of the resulting models make
                 evolutionary methods very attractive for supervised
                 learning applications in bioinformatics. However, their
                 characteristics also make them highly susceptible to
                 overtraining or to discovering chance relationships in
                 the data. Identification of appropriate criteria for
                 terminating evolution and for selecting an
                 appropriately validated model is vital. Some approaches
                 that are commonly applied to other modeling methods are
                 not necessarily applicable in a straightforward manner
                 to evolutionary methods. An approach to model selection
                 is presented that is not unduly computationally
                 intensive. To illustrate the issues and the technique
                 two bioinformatic datasets are used, one relating to
                 metabolite determination and the other to disease
                 prediction from gene expression data.",
  notes =        "PMID: 14642667",

Genetic Programming entries for Jem J Rowland