Recursion, Lambda Abstractions and Genetic Programming

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

@InProceedings{yu:1998:rlaGP98,
  author =       "Tina Yu and Chris Clack",
  title =        "Recursion, Lambda Abstractions and Genetic
                 Programming",
  booktitle =    "Genetic Programming 1998: Proceedings of the Third
                 Annual Conference",
  year =         "1998",
  editor =       "John R. Koza and Wolfgang Banzhaf and 
                 Kumar Chellapilla and Kalyanmoy Deb and Marco Dorigo and 
                 David B. Fogel and Max H. Garzon and 
                 David E. Goldberg and Hitoshi Iba and Rick Riolo",
  pages =        "422--431",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "San Francisco, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-548-7",
  URL =          "http://www.cs.mun.ca/~tinayu/index_files/addr/public_html/Recursion.pdf",
  URL =          "ftp://bells.cs.ucl.ac.uk/functional/papers/Published/Recursion.ps.gz",
  size =         "9 pages",
  abstract =     "Module creation and reuse are essential for Genetic
                 Programming (GP) to be effective with larger and more
                 complex problems. This paper presents a particular kind
                 of program structure to serve these purposes: modules
                 are represented as lambda abstractions and their reuse
                 is achieved through an implicit recursion. A type
                 system is used to preserve this structure. The
                 structure of lambda abstraction and implicit recursion
                 also provides structure abstraction in the program.
                 Since the GP paradigm evolves program structure and
                 contents simultaneously, structure abstraction can
                 reduce the search effort for good program structure.
                 Most evolutionary effort is then focused on the search
                 for correct program contents rather than the structure.
                 Experiments on the Even-N-Parity problem show that,
                 with the structure of lambda abstractions and implicit
                 recursion, GP is able to find a general solution which
                 works for any value of N very efficiently.",
  notes =        "GP-98 slides
                 http://www.cs.mun.ca/~tinayu/index_files/addr/public_html/Recursion.slide.pdf",
}

Genetic Programming entries for Tina Yu Christopher D Clack

Citations