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A Search for Hidden Relationships: Data Mining with Genetic Algorithms

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  • Szpiro, George G
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    Abstract

    This paper presents an algorithm that permits the search for dependencies among sets of data (univariate or multivariate time-series, or cross-sectional observations). The procedure is modeled after genetic theories and Darwinian concepts, such as natural selection and survival of the fittest. It permits the discovery of equations of the data-generating process in symbolic form. The genetic algorithm that is described here uses parts of equations as building blocks to breed ever better formulas. Apart from furnishing a deeper understanding of the dynamics of a process, the method also permits global predictions and forecasts. The algorithm is successfully tested with artificial and with economic time-series and also with cross-sectional data on the performance and salaries of NBA players during the 94-95 season. Citation Copyright 1997 by Kluwer Academic Publishers.

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    File URL: http://journals.kluweronline.com/issn/0927-7099/contents
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    Bibliographic Info

    Article provided by Society for Computational Economics in its journal Computational Economics.

    Volume (Year): 10 (1997)
    Issue (Month): 3 (August)
    Pages: 267-77

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    Handle: RePEc:kap:compec:v:10:y:1997:i:3:p:267-77

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    Cited by:
    1. Beenstock, Michael & Szpiro, George, 2002. "Specification search in nonlinear time-series models using the genetic algorithm," Journal of Economic Dynamics and Control, Elsevier, vol. 26(5), pages 811-835, May.
    2. Marcos Álvarez-Díaz & Alberto Álvarez, 2003. "Predicción No-Lineal De Tipos De Cambio: Algoritmos Genéticos, Redes Neuronales Y Fusión De Datos," Working Papers 0301, Universidade de Vigo, Departamento de Economía Aplicada.
    3. Marcos Álvarez-Díaz & Alberto Álvarez, 2002. "Predicción No-Lineal De Tipos De Cambio: Algoritmos Genéticos, Redes Neuronales Y Fusión De Datos," Working Papers 0205, Universidade de Vigo, Departamento de Economía Aplicada.

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