Genetic programming as a model induction engine

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Vladan Babovic and Maarten Keijzer",
  title =        "Genetic programming as a model induction engine",
  journal =      "Journal of Hydroinformatics",
  year =         "2000",
  volume =       "1",
  number =       "1",
  pages =        "35--60",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming, data mining,
                 knowledge discovery",
  ISSN =         "1464-7141",
  URL =          "",
  URL =          "",
  size =         "26 pages",
  abstract =     "Present day instrumentation networks already provide
                 immense quantities of data, very little of which
                 provides any insights into the basic physical processes
                 that are occurring in the measured medium. This is to
                 say that the data by itself contributes little to the
                 knowledge of such processes. Data mining and knowledge
                 discovery aim to change this situation by providing
                 technologies that will greatly facilitate the mining of
                 data for knowledge. In this new setting the role of a
                 human expert is to provide domain knowledge, interpret
                 models suggested by the computer and devise further
                 experiments that will provide even better data
                 coverage. Clearly, there is an enormous amount of
                 knowledge and understanding of physical processes that
                 should not be just thrown away. Consequently, we
                 strongly believe that the most appropriate way forward
                 is to combine the best of the two approaches:
                 theory-driven, understanding-rich with data-driven
                 discovery process. This paper describes a particular
                 knowledge discovery algorithm Genetic Programming (GP).
                 Additionally, an augmented version of GP -
                 dimensionally aware GP - which is arguably more useful
                 in the process of scientific discovery is described in
                 great detail. Finally, the paper concludes with an
                 application of dimensionally aware GP to a problem of
                 induction of an empirical relationship describing the
                 additional resistance to flow induced by flexible
  notes =        "dimensionally aware GP. Additional river water flow
                 resistance caused by flexible vegetation closure and
                 strong typing (STGP). dimensionally aware brood
                 selection. Kutija-Hong model.",

Genetic Programming entries for Vladan Babovic Maarten Keijzer