Genetically evolved models and normality of their fitted residuals

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Article{kaboudan:2000:gemnfr,

• author = "M. A. Kaboudan",
• title = "Genetically evolved models and normality of their fitted residuals",
• journal = "Journal of Economic Dynamics and Control",
• year = "2001",
• volume = "25",
• number = "11",
• pages = "1719--1749",
• month = "1 " # nov,
• organisation = "Society for Computational Economics",
• email = "Mahmoud_Kaboudan@Redlands.edu",
• keywords = "genetic algorithms, genetic programming, Model evaluation, Sunspot numbers, Canadian lynx data",
• URL = "http://www.sciencedirect.com/science/article/B6V85-43DKSHS-2/1/814779519703b0e20b2ed476f932e7e5",
• DOI = "doi:10.1016/S0165-1889(00)00004-X",
• size = "31 pages",
• abstract = "This paper evaluates performance of genetically evolved models. GPQuick, a genetic programming software written in C++, is used to evolve best-fit regression models for simulated and real world data. Simulated data are twelve time series with different but known dynamical structures. Predicted values from best models are compared with originally simulated data and the residuals are statistically evaluated. The results suggest that genetic programming approximates less complex and less noisy data better than it does more complex and noisy data. GPQuick is then used to evolve models of real world data extracted from Canadian lynx and sunspot numbers.",
• notes = "JEL Classification: C63; C45; C52. cf. CEF'2000.",

}

Genetic Programming entries for Mahmoud A Kaboudan

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