Evolutionary Higher-Order Concept Learning

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Claire J. Kennedy",
  title =        "Evolutionary Higher-Order Concept Learning",
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
  year =         "1998",
  editor =       "John R. Koza",
  pages =        "113 and 258",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.bris.ac.uk/Publications/Papers/1000281.pdf",
  size =         "1+1 page",
  abstract =     "Current concept learners are limited in their
                 applicability as they generally rely on comparatively
                 poor knowledge representation facilities (e.g.
                 attribute value pairs, flattened horn clauses). The
                 work carried out in support of my thesis has involved
                 extending concept learning to a higher order setting by
                 developing a novel representation based on closed
                 Escher terms for highly structured data. The added
                 expressiveness offered by the proposed representation
                 results in an explosion of the search space, which is
                 compounded by the increased complexity of its
                 structure. This paper describes an investigation into
                 the use of genetic programming techniques to allow the
                 exploitation of higher-order features during the
                 induction of structured concept descriptions.",
  notes =        "GP-98LB, GP-98PhD Student Workshop",

Genetic Programming entries for Claire J Kennedy