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Tinkering with Genetic Algorithms: Forecasting and Data Mining in Finance and Economics

  • George G. Szpiro
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 100)

Abstract

In two previous papers [13,14] genetic algorithms were presented that permit the search for dependencies among sets of data (univariate or multivariate time-series, or cross-sectional observations) . These algorithms — modeled after genetic theories and Darwinian concepts, such as natural selection and survival of the fittest — permit the discovery of equations, in symbolic form, that re-create or, at least, mimic the data-generating process. This paper discusses some of the computational issues and difficulties that may arise when the genetic algorithm is applied, and suggests ways to improve the algorithm’s performance.

Keywords

Genetic Algorithm Hide Relationship Artificial Series Darwinian Concept Correct Functional Form 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • George G. Szpiro
    • 1
  1. 1.Israeli Center for Academic StudiesUniversity of ManchesterKiriat OnoIsrael

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