A Search for Hidden Relationships: Data Mining with Genetic Algorithms

  • Authors:
  • George G. Szpiro

  • Affiliations:
  • Israeli Centre for Academic Studies, Kiriat Ono, Israel (Author‘s address: POB 6278, Jerusalem, Israel, e-mail: george@netvision.net.il)

  • Venue:
  • Computational Economics - Special issue on computational economics in Geneva: volume 1: computational econometrics, statistics, and optimization
  • Year:
  • 1997

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper presents an algorithm that permits thesearch for dependencies among sets of data (univariateor multivariate time-series, or cross-sectionalobservations). The procedure is modeled after genetictheories and Darwinian concepts, such as naturalselection and survival of the fittest. It permits thediscovery of equations of the data-generating processin symbolic form. The genetic algorithm that isdescribed here uses parts of equations as buildingblocks to breed ever better formulas. Apart fromfurnishing a deeper understanding of the dynamics ofa process, the method also permits global predictionsand forecasts. The algorithm is successfully testedwith artificial and with economic time-series and alsowith cross-sectional data on the performance andsalaries of NBA players during the 94–95 season.