Learning Concept Descriptions with Typed Evolutionary Programming

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

  author =       "Claire J. Thie and Christophe Giraud-Carrier",
  title =        "Learning Concept Descriptions with Typed Evolutionary
  journal =      "IEEE Transactions on Knowledge and Data Engineering",
  volume =       "17",
  number =       "12",
  year =         "2005",
  ISSN =         "1041-4347",
  pages =        "1664--1677",
  publisher =    "IEEE Computer Society",
  address =      "Los Alamitos, CA, USA",
  keywords =     "genetic algorithms, genetic programming, STGP, Concept
                 learning, typed evolutionary programming",
  DOI =          "doi:10.1109/TKDE.2005.199",
  abstract =     "Examples and concepts in traditional concept learning
                 tasks are represented with the attribute-value
                 language. While enabling efficient implementations, we
                 argue that such propositional representation is
                 inadequate when data is rich in structure. This paper
                 describes STEPS, a strongly-typed evolutionary
                 programming system designed to induce concepts from
                 structured data. STEPS' higher-order logic
                 representation language enhances expressiveness, while
                 the use of evolutionary computation dampens the effects
                 of the corresponding explosion of the search space.
                 Results on the PTE2 challenge, a major real-world
                 knowledge discovery application from the molecular
                 biology domain, demonstrate promise.",
  notes =        "Claire Julia Kennedy",

Genetic Programming entries for Claire J Kennedy Christophe Giraud-Carrier