Using Fitness Distributions to Improve the Evolution of Learning Structures

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

@InProceedings{igel:1999:UFDIELS,
  author =       "Christian Igel and Martin Kreutz",
  title =        "Using Fitness Distributions to Improve the Evolution
                 of Learning Structures",
  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 =       "3",
  pages =        "1902--1909",
  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, fitness
                 distributions, density estimation, gradient-based
                 operators, absolute benefit, coefficient adaptation,
                 density estimation models, fitness distributions,
                 fitness space, fitness trajectory analysis, gradient
                 based operators, gradient information, information
                 theory based measure, learning structure evolution,
                 offline analysis, online operator adaptation,
                 information theory, learning (artificial intelligence),
                 probability",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  URL =          "http://www.neuroinformatik.ruhr-uni-bochum.de/ini/PEOPLE/igel/UFDtItEoLS.ps.gz",
  URL =          "http://citeseer.ist.psu.edu/294668.html",
  DOI =          "doi:10.1109/CEC.1999.785505",
  abstract =     "the absolute benefit, a measure of improvement in the
                 fitness space, is derived from the viewpoint of fitness
                 distribution and fitness trajectory analysis. It is
                 used for online operator-adaptation, where the
                 optimisation of density estimation models serves as an
                 example. A new information theory based measure is
                 proposed to judge the accuracy of the evolved models.
                 Further, the absolute benefit is applied to offline
                 analysis of new gradient based operators used for
                 coefficient adaptation in genetic programming. An
                 efficient method to calculate the gradient information
                 is presented.",
  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 Christian Igel Martin Kreutz

Citations