Genetic Evolution of Regression Models for Business and Economic Forecasting

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "M. A. Kaboudan",
  title =        "Genetic Evolution of Regression Models for Business
                 and Economic Forecasting",
  booktitle =    "Proceedings of the Congress on Evolutionary
  year =         "1999",
  editor =       "Peter J. Angeline and Zbyszek Michalewicz and 
                 Marc Schoenauer and Xin Yao and Ali Zalzala",
  volume =       "2",
  pages =        "1260--1268",
  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, forecasting,
                 business, complex systems, data fitting, economic
                 forecasting, economics researchers, evolutionary
                 computer programs, evolutionary methodology,
                 evolutionary methods, genetic evolution, output files,
                 regression models, scientific research, statistical
                 tests, business data processing, economics, forecasting
                 theory, statistical analysis",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  DOI =          "doi:10.1109/CEC.1999.782587",
  abstract =     "The paper attempts to bridge the gap between genetic
                 evolution of regression models and their use in
                 business and economic forecasting. With ample evidence
                 of their successful fitting of data from fairly complex
                 systems, a logical next step is to make genetic and
                 evolutionary methods useful and available to business
                 and economics researchers. A few suggestions are made;
                 they describe desirable output files and statistical
                 tests to evaluate results from evolved models which
                 genetic or evolutionary computer programs should
                 produce. These suggestions should invite better ones to
                 popularise use of evolutionary methodology and to
                 benefit scientific research",
  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 Mahmoud A Kaboudan