A Search for Hidden Relationships: Data Mining with Genetic Algorithms |
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Authors: | SZPIRO GEORGE G. |
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Affiliation: | (1) Israeli Centre for Academic Studies, Kiriat Ono, Israel;(2) POB 6278, Jerusalem, Israel |
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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. |
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Keywords: | data mining forecasting genetic algorithms. |
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