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A Search for Hidden Relationships: Data Mining with Genetic Algorithms

  • Szpiro, George G
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    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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    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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