Variants of genetic programming for species distribution modelling -- fitness sharing, partial functions, population evaluation

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@Article{McKay:2001:EM,
  author =       "R. I. (Bob) McKay",
  title =        "Variants of genetic programming for species
                 distribution modelling -- fitness sharing, partial
                 functions, population evaluation",
  year =         "2001",
  journal =      "Ecological Modelling",
  volume =       "146",
  pages =        "231--241",
  number =       "1-3",
  keywords =     "genetic algorithms, genetic programming, Fitness
                 sharing, Species distribution, Spatial learning",
  ISSN =         "0304-3800",
  URL =          "http://www.sciencedirect.com/science/article/B6VBS-44HYNCP-N/1/a4ef72e29b6f89efd2ddb1b22258ef06",
  DOI =          "doi:10.1016/S0304-3800(01)00309-X",
  abstract =     "We investigate the use of partial functions, fitness
                 sharing and committee learning in genetic programming.
                 The primary intended application of the work is in
                 learning spatial relationships for ecological
                 modelling. The approaches are evaluated using a
                 well-studied ecological modelling problem, the greater
                 glider population density problem. Combinations of the
                 three treatments (partial functions, fitness sharing
                 and committee learning) are compared on the dimensions
                 of accuracy and computational cost. Fitness sharing
                 significantly improves learning accuracy, and
                 populations of partial functions substantially reduce
                 computational cost. The results of committee learning
                 are more equivocal, and require further investigation.
                 The learned models are highly predictive, but also
                 highly explanatory.",
}

Genetic Programming entries for R I (Bob) McKay

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