Evolving Recursive Functions for the Even-Parity Problem Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@InCollection{wong:1996:aigp2,
  author =       "Man Leung Wong and Kwong Sak Leung",
  title =        "Evolving Recursive Functions for the Even-Parity
                 Problem Using Genetic Programming",
  booktitle =    "Advances in Genetic Programming 2",
  publisher =    "MIT Press",
  year =         "1996",
  editor =       "Peter J. Angeline and K. E. {Kinnear, Jr.}",
  pages =        "221--240",
  chapter =      "11",
  address =      "Cambridge, MA, USA",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-262-01158-1",
  URL =          "http://cisnet.mit.edu/Advances-in-Genetic-Programming/238",
  abstract =     "One of the most important and challenging areas of
                 research in evolutionary algorithms is to investigate
                 ways to successfully apply evolutionary algorithms to
                 larger and more complicated problems. One approach to
                 make a given problem more tractable is to discover
                 problem representations automatically. Koza (1993) uses
                 the even-n-parity problem to demonstrate extensively
                 that his approach of Automatic Function Definition
                 (ADF) can facilitate the solution of the problem.
                 Unfortunately, the solutions found by GP with ADF can
                 only solved the problem for a particular value of n. If
                 a different value of n is used, GP with ADF must be
                 used again to find other programs that can solve the
                 new even-n-parity problem. Clearly, the solution found
                 is not general enough to solve all even-n-parity
                 problem for n greater than or equal to zero. In this
                 paper, we apply GGP (Generic Genetic Programming) to
                 evolve general recursive functions for the
                 even-n-parity problem. GGP is very flexible and
                 programs in various programming languages can be
                 acquired. Moreover, it is powerful enough to represent
                 context-sensitive information and domain-dependent
                 knowledge. This knowledge can be used to accelerate the
                 learning speed and/or improve the quality of the
                 programs induced. A number of experiments have been
                 performed to determine the impact of domain-specific
                 knowledge on the speed of learning.",
}

Genetic Programming entries for Man Leung Wong Kwong-Sak Leung