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.
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SZPIRO, G.G. A Search for Hidden Relationships: Data Mining with Genetic Algorithms. Computational Economics 10, 267–277 (1997). https://doi.org/10.1023/A:1008673309609
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DOI: https://doi.org/10.1023/A:1008673309609