- author = "M. A. Kaboudan",
- title = "Genetic Evolution of Regression Models for Business and Economic Forecasting",
- 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 = "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",
