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On the genetic algorithm with adaptive mutation rate and selected statistical applications

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  • André Pereira
  • Bernardo Andrade

Abstract

We give sufficient conditions which the mutation rate must satisfy for the convergence of the genetic algorithm when that rate is allowed to change throughout iterations. The empirical performance of the algorithm with regards to changes in the mutation parameter is explored via test functions, ARIMA model selection and maximum likelihood estimation illustrating the advantages of letting the mutation rate decrease from rather unusual high values to the commonly used low ones. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • André Pereira & Bernardo Andrade, 2015. "On the genetic algorithm with adaptive mutation rate and selected statistical applications," Computational Statistics, Springer, vol. 30(1), pages 131-150, March.
    • Handle: RePEc:spr:compst:v:30:y:2015:i:1:p:131-150
    DOI: 10.1007/s00180-014-0526-x
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    References listed on IDEAS

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    1. Joshua Hallam & Olcay Akman & Füsun Akman, 2010. "Genetic algorithms with shrinking population size," Computational Statistics, Springer, vol. 25(4), pages 691-705, December.
    2. Per Hokstad, 1983. "A Method For Diagnostic Checking Of Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 4(3), pages 177-183, May.
    3. Angela Noufaily & M. Jones, 2013. "On maximization of the likelihood for the generalized gamma distribution," Computational Statistics, Springer, vol. 28(2), pages 505-517, April.
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    Cited by:

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