Subsymbolic methods for data mining in hydraulic engineering

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

@Article{Minns:2000:JH,
  author =       "Anthony W. Minns",
  title =        "Subsymbolic methods for data mining in hydraulic
                 engineering",
  journal =      "Journal of Hydroinformatics",
  year =         "2000",
  volume =       "2",
  number =       "1",
  pages =        "3--13",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1464-7141",
  URL =          "http://www.iwaponline.com/jh/002/0003/0020003.pdf",
  size =         "11 pages",
  abstract =     "This paper describes the results of experiments with
                 artificial neural networks (ANNs) and genetic
                 programming (GP) applied to some problems of data
                 mining. It is shown how these subsymbolic methods can
                 discover usable relations in measured and experimental
                 data with little or no a priori knowledge of the
                 governing physical process characteristics. On the one
                 hand, the ANN does not explicitly identify a form of
                 model but this form is implicit in the ANN, being
                 encoded within the distribution of weights. However, in
                 cases where the exact form of the empirical relation is
                 not considered as important as the ability of the
                 formula to map the experimental data accurately, the
                 ANN provides a very efficient approach. Furthermore, it
                 is demonstrated how numerical schemes, and thus partial
                 differential equations, may be derived directly from
                 data by interpreting the weight distribution within a
                 trained ANN. On the other hand, GP evolutionary force
                 is directed towards the creation of models that take a
                 symbolic form. The resulting symbolic expressions are
                 generally less accurate than the ANN in mapping the
                 experimental data, however, these expressions may
                 sometimes be more easily examined to provide insight
                 into the processes that created the data. An example is
                 used to demonstrate how GP can generate a wide variety
                 of formulae, of which some may provide genuine insight
                 while others may be quite useless.",
}

Genetic Programming entries for Anthony W Minns

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