Learning Recursive Functions from Noisy Examples using Generic Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Man Leung Wong and Kwong Sak Leung",
  title =        "Learning Recursive Functions from Noisy Examples using
                 Generic Genetic Programming",
  booktitle =    "Genetic Programming 1996: Proceedings of the First
                 Annual Conference",
  editor =       "John R. Koza and David E. Goldberg and 
                 David B. Fogel and Rick L. Riolo",
  year =         "1996",
  month =        "28--31 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  pages =        "238--246",
  address =      "Stanford University, CA, USA",
  publisher =    "MIT Press",
  size =         "9 pages",
  URL =          "http://cptra.ln.edu.hk/~mlwong/conference/gp1996.pdf",
  URL =          "http://cognet.mit.edu/sites/default/files/books/9780262315876/pdfs/9780262315876_chap29.pdf",
  URL =          "http://cognet.mit.edu/library/books/view?isbn=0262611279",
  abstract =     "One of the most important and challenging areas of
                 research in evolutionary algorithms is the
                 investigation of ways to successfully apply
                 evolutionary algorithms to larger and more complicated
                 problems. In this paper, we apply GGP (Generic Genetic
                 Programming) to evolve general recursive functions for
                 the even-n-parity problem from noisy training examples.
                 GGP is very flexible and programs in various
                 programming languages can be acquired. Moreover, it is
                 powerful enough to handle context-sensitive information
                 and domain-dependent knowledge. A number of experiments
                 have been performed to determine the impact of noise in
                 training examples on the speed of learning.",
  notes =        "GP-96",

Genetic Programming entries for Man Leung Wong Kwong-Sak Leung