Phase Transition and New Fitness Function Based Genetic Inductive Logic Programming Algorithm

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@InProceedings{Li:2012:CECb,
  title =        "Phase Transition and New Fitness Function Based
                 Genetic Inductive Logic Programming Algorithm",
  author =       "Yanjuan Li and Maozu Guo",
  pages =        "956--963",
  booktitle =    "Proceedings of the 2012 IEEE Congress on Evolutionary
                 Computation",
  year =         "2012",
  editor =       "Xiaodong Li",
  month =        "10-15 " # jun,
  DOI =          "doi:10.1109/CEC.2012.6256626",
  size =         "8 pages",
  address =      "Brisbane, Australia",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming, Learning
                 classifier systems, machine learning, inductive logic
                 programming, genetic inductive logic programming",
  abstract =     "A new genetic inductive logic programming (GILP for
                 short) algorithm named PT- NFF-GILP (Phase Transition
                 and New Fitness Function based Genetic Inductive Logic
                 Programming) is proposed in this paper. Based on phase
                 transition of the covering test, PT-NFF-GILP randomly
                 generates initial population in phase transition region
                 instead of the whole space of candidate clauses.
                 Moreover, a new fitness function, which not only
                 considers the number of examples covered by rules, but
                 also considers the ratio of the examples covered by
                 rules to the training examples, is defined in
                 PT-NFF-GILP. The new fitness function measures the
                 quality of first-order rules more precisely, and
                 enhances the search performance of algorithm.
                 Experiments on ten learning problems show that: 1) the
                 new method of generating initial population can
                 effectively reduce iteration number and enhance
                 predictive accuracy of GILP algorithm; 2) the new
                 fitness function measures the quality of first-order
                 rules more precisely and avoids generating
                 over-specific hypothesis; 3) The performance of
                 PT-NFF-GILP is better than other algorithms compared
                 with it, such as G-NET, KFOIL and NFOIL.",
  notes =        "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the
                 EPS and the IET.",
}

Genetic Programming entries for Yanjuan Li Maozu Guo

Citations