Genetic Programming Approach to Hierarchical Production Rule Discovery

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

@Article{Al-Maqaleh:2007:isi,
  author =       "Basheer M. Al-Maqaleh and Kamal K. Bharadwaj",
  title =        "Genetic Programming Approach to Hierarchical
                 Production Rule Discovery",
  journal =      "International Science Index",
  year =         "2007",
  volume =       "1",
  number =       "11",
  pages =        "531--534",
  keywords =     "genetic algorithms, genetic programming, hierarchy,
                 knowledge discovery in database, subsumption matrix.
                 k",
  publisher =    "World Academy of Science, Engineering and Technology",
  index =        "International Science Index 11, 2007",
  bibsource =    "http://waset.org/Publications",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:10.1.1.308.1481",
  ISSN =         "1307-6892",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.308.1481",
  URL =          "http://waset.org/publications/10022",
  size =         "4 pages",
  abstract =     "Automated discovery of hierarchical structures in
                 large data sets has been an active research area in the
                 recent past. This paper focuses on the issue of mining
                 generalised rules with crisp hierarchical structure
                 using Genetic Programming (GP) approach to knowledge
                 discovery. The post-processing scheme presented in this
                 work uses flat rules as initial individuals of GP and
                 discovers hierarchical structure. Suitable genetic
                 operators are proposed for the suggested encoding.
                 Based on the Subsumption Matrix(SM), an appropriate
                 fitness function is suggested. Finally, Hierarchical
                 Production Rules (HPRs) are generated from the
                 discovered hierarchy. Experimental results are
                 presented to demonstrate the performance of the
                 proposed algorithm.",
}

Genetic Programming entries for Basheer Mohamad Ahmad Al-Maqaleh K K Bharadwaj

Citations