Inductive Learning with Inductive Logic Programming and Genetic Programming

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

@Article{Ichise:1999:JJSAI,
  author =       "Ryutaro Ichise and Masayuki Numao",
  title =        "Inductive Learning with Inductive Logic Programming
                 and Genetic Programming",
  journal =      "Journal of Japanese Society for Artificial
                 Intelligence",
  year =         "1999",
  volume =       "14",
  number =       "2",
  pages =        "307--314",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, ILP",
  ISSN =         "0912-8085",
  URL =          "http://sciencelinks.jp/j-east/article/199911/000019991199A0325870.php",
  URL =          "http://www.ai-gakkai.or.jp/en/vol14_no2/",
  abstract =     "Two approaches to inducing a concept represented in
                 first order logic are inductive logic programming(ILP)
                 and genetic programming(GP). In ILP, concept learning
                 can be considered as a search in the space specified by
                 the background knowledge, and in which the goal concept
                 is represented by Horn clauses. On the other hand, in
                 GP, the search space is specified by terminal and
                 nonterminal symbols, and the goal is represented
                 generally by S-expressions. These two approaches are
                 very similar in terms of their methods and goals, yet
                 their combination in previous work is rare. In this
                 paper, we propose a method that synthesises the
                 inductive logic programming and genetic programming
                 approaches. The concept behind this approach is to
                 combine the search method of GP, that is, Genetic
                 Algorithm, with the type and mode methods of ILP. We
                 have implemented a system called SYNGIP (SYNthesized
                 system with Genetic programming and Inductive logic
                 Programming) based on the method. Experimental results
                 show that the proposed method can be used to treat, in
                 the same way, learning from training examples that do
                 not have discrete classes, and learning from both
                 positive and negative training examples. Moreover, the
                 proposed method constitutes a novel solution to the
                 closure problem and provides a new bias for concept
                 learning. (author abst.)",
  notes =        "Language=Japanese Journal Code=X0330A Accession
                 number=99A0325870

                 Tokyo Inst. of Technology, Graduate School",
}

Genetic Programming entries for Ryutaro Ichise Masayuki Numao

Citations