Generalized Function Analysis Using Hybrid Evolutionary Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{hafner:1999:GFAUHEA,
  author =       "Christian Hafner and Jurg Frohlich",
  title =        "Generalized Function Analysis Using Hybrid
                 Evolutionary Algorithms",
  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 =       "1",
  pages =        "287--294",
  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, time series,
                 evolutionary computation, generalized function
                 analysis, hybrid evolutionary algorithms, time series
                 prediction, prominent codes, future data, symbolic
                 regression, series expansions, parameter optimization
                 techniques, highly complex codes, physics, economy",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  URL =          "http://ieeexplore.ieee.org/iel5/6342/16952/00781938.pdf",
  DOI =          "doi:10.1109/CEC.1999.781938",
  size =         "8 pages",
  abstract =     "Two novel codes for the prediction of time series are
                 presented. Unlike most of the prominent codes based on
                 finding a process that predicts the future data, these
                 codes are based on function analysis and symbolic
                 regression. Both codes are based on a generalization
                 and combination of series expansions, parameter
                 optimization techniques, and genetic programming. These
                 highly complex codes are outlined and applied to
                 different examples of physics and economy.",
  notes =        "CEC-99 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 Library of Congress Number = 99-61143 Extrapolation.
                 GCP v. EGP. Sunspot, Dow Jones, stock price prediction.
                 Full Binary trees of depth 3.

                 On http://alphard.ethz.ch/Hafner/ggp/gp.htm there is
                 more information on GGP with links for downloading the
                 software.",
}

Genetic Programming entries for Christian Hafner Jurg Frohlich

Citations