A Search for Hidden Relationships : Data Mining with Genetic Algorithms

  title={A Search for Hidden Relationships : Data Mining with Genetic Algorithms},
  author={George G. Szpiro},
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… CONTINUE READING

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Publications referenced by this paper.
Showing 1-10 of 10 references

Using genetic algorithms to find technical trading rules, working paper, Rodney L

F. Allen, R. Karjalainen
View 4 Excerpts
Highly Influenced

Forecasting chaotic time series with genetic algorithms

G. G. Szpiro
Phys. Rev. E, • 1997

Genetic programming, MIT Press, Cambridge

J. R. Koza
View 1 Excerpt

Nonlinear forecasts for the S&P stock index’, in Casdagli and Eubanks (eds), Nonlinear modeling and forecasting

B. LeBaron
Santa Fe Institute, • 1992
View 1 Excerpt

Nonlinear dynamics and stock returns

J. A. Scheinkman, B. LeBaron
J. of Business, • 1989
View 1 Excerpt

Predicting chaotic time series.

Physical review letters • 1987
View 2 Excerpts

Adaptation in natural and artificial systems, University of Michigan Press, Ann Arbor

J. H. Holland
View 1 Excerpt

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