Neural Logic Network Learning using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Chew Lim Tan and Henry Wai Kit Chia",
  title =        "Neural Logic Network Learning using Genetic
  year =         "2001",
  editor =       "Bernhard Nebel",
  ISBN =         "1-55860-777-3",
  bibsource =    "DBLP,",
  booktitle =    "Proceedings of the Seventeenth International Joint
                 Conference on Artificial Intelligence, IJCAI 2001",
  pages =        "803--808",
  address =      "Seattle, USA",
  month =        aug # " 4-10",
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  size =         "6 pages",
  abstract =     "Neural Logic Network or Neulonet is a hybrid of neural
                 network expert systems. Its strength lies in its
                 ability to learn and to represent human logic in
                 decision making using component net rules. The
                 technique originally employed in neulonet learning is
                 backpropagation. However, the resulting weight
                 adjustments will lead to a loss in the logic of the net
                 rules. A new technique is now developed that allows the
                 neulonet to learn by composing net rules using genetic
                 programming. This paper presents experimental results
                 to demonstrate this new and exciting capability in
                 capturing human decision logic from examples.
                 Comparisons will also be made between the use of net
                 rules, and the use of standard Boolean logic of
                 negation, disjunction and conjunction in evolutionary
  notes =        "",

Genetic Programming entries for Chew-Lim Tan Henry Wai-Kit Chia