A useful tool for statistical estimation: Genetic algorithms

Joseph M. Pasia, Augusto Y. Hermosilla, Hernando Ombao

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


In this article, we introduce genetic algorithms (GAs) as a viable tool in estimating parameters in a wide array of statistical models. We performed simulation studies that compared the bias and variance of GAs with classical tools, namely, the steepest descent, Gauss-Newton, Levenberg-Marquardt and don't use derivative methods. In our simulation studies, we used the least squares criterion as the optimizing function. The performance of the GAs and classical methods were compared under the logistic regression model; non-linear Gaussian model and non-linear non-Gaussian model. We report that the GAs' performance is competitive to the classical methods under these three models.

Original languageEnglish (US)
Pages (from-to)237-251
Number of pages15
JournalJournal of Statistical Computation and Simulation
Issue number4
StatePublished - Apr 2005
Externally publishedYes


  • Gauss-Newton method
  • Genetic algorithms
  • Least squares criterion
  • Logistic model
  • Non-linear and non-Gaussian models
  • Non-linear regression

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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