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Genetic Algorithms for Estimation Problems with Multiple Optima, Nondifferentiability, and Other Irregular Features

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  • Dorsey, Robert E
  • Mayer, Walter J

Abstract

The genetic algorithm is examined as a method for solving optimization problems in econometric estimation. It does not restrict either the form or regulalrity of the objective function, allows a reasonbly large parameter space, and does not rely on a point-to-point search. The performance is evaluated through two sets of experiments on standard test problems as well as econometric problems from the literature. First, alternative genetic algorithms are contrasted that vary over mutation and crossover rates, population sizes, and other features. Second, the genetic algorithm is compared to Nelder-Mead simplex, simulated annealing, adaptive random search, and MSCORE.

Suggested Citation

  • Dorsey, Robert E & Mayer, Walter J, 1995. "Genetic Algorithms for Estimation Problems with Multiple Optima, Nondifferentiability, and Other Irregular Features," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 53-66, January.
  • Handle: RePEc:bes:jnlbes:v:13:y:1995:i:1:p:53-66
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