Efficient Genetic Programming for Finding Good Generalizing Boolean Functions

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Stefan Droste",
  title =        "Efficient Genetic Programming for Finding Good
                 Generalizing {Boolean} Functions",
  booktitle =    "Genetic Programming 1997: Proceedings of the Second
                 Annual Conference",
  editor =       "John R. Koza and Kalyanmoy Deb and Marco Dorigo and 
                 David B. Fogel and Max Garzon and Hitoshi Iba and 
                 Rick L. Riolo",
  year =         "1997",
  month =        "13-16 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  pages =        "82--87",
  address =      "Stanford University, CA, USA",
  publisher_address = "San Francisco, CA, USA",
  publisher =    "Morgan Kaufmann",
  URL =          "https://eldorado.uni-dortmund.de/dspace/bitstream/2003/5323/1/gp97.pdf",
  URL =          "http://citeseer.ist.psu.edu/326196.html",
  size =         "pages",
  abstract =     "This paper shows how genetic programming (GP) can help
                 in finding generalizing Boolean functions when only a
                 small part of the function values are given. The
                 selection pressure favours functions having as few
                 subfunctions as possible while only using essential
                 variables, so the resulting functions should have good
                 generalization properties. For efficiency no
                 S-expressions are used for representation, but a
                 special case of directed acyclic graphs known as
                 ordered binary decision diagrams (OBDDs), making it
                 possible to learn the 20-multiplexer.",
  notes =        "GP-97",

Genetic Programming entries for Stefan Droste