AIM-GP and Parallelism

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

@InProceedings{nordin:1999:AP,
  author =       "Peter Nordin and Frank Hoffmann and 
                 Frank D. Francone and Markus Brameier and Wolfgang Banzhaf",
  title =        "AIM-GP and Parallelism",
  booktitle =    "Proceedings of the Congress on Evolutionary
                 Computation",
  year =         "1999",
  editor =       "Peter J. Angeline and Zbyszek Michalewicz and 
                 Marc Schoenauer and Xin Yao and Ali Zalzala",
  volume =       "2",
  pages =        "1059--1066",
  address =      "Mayflower Hotel, Washington D.C., USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "Congress on Evolutionary Computation, IEEE / Neural
                 Networks Council, Evolutionary Programming Society,
                 Galesia, IEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, parallel and
                 distributed processing, AIM-GP system, Automatic
                 Induction of Machine Code with Genetic Programming ,
                 demes, fast system, machine learning tasks, parallel
                 implementation, parallelization, tree based systems,
                 automatic programming, genetic algorithms, learning
                 (artificial intelligence), parallel algorithms,
                 parallel programming",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  URL =          "http://citeseer.ist.psu.edu/cache/papers/cs/29615/http:zSzzSzls11-www.informatik.uni-dortmund.dezSzpeoplezSzbanzhafzSzcec_parallel.pdf/aim-gp-and-parallelism.pdf",
  URL =          "http://citeseer.ist.psu.edu/600901.html",
  DOI =          "doi:10.1109/CEC.1999.782540",
  abstract =     "Many machine learning tasks are just too hard to be
                 solved with a single processor machine, no matter how
                 efficient the algorithms are and how fast our hardware
                 is. Luckily genetic programming is well suited for
                 parallelisation compared to standard serial algorithms.
                 The paper describes the first parallel implementation
                 of an AIM-GP system, creating the potential for an
                 extremely fast system. The system is tested on three
                 problems and several variants of demes and migration
                 are evaluated. Most of the results are applicable to
                 both linear and tree based systems",
  notes =        "CEC-99 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 Library of Congress Number = 99-61143",
}

Genetic Programming entries for Peter Nordin Frank Hoffmann Frank D Francone Markus Brameier Wolfgang Banzhaf

Citations