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

André Pereira () and Bernardo Andrade ()

Computational Statistics, 2015, vol. 30, issue 1, 150 pages

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

Keywords: Markov chains; Evolutionary algorithms; Model selection; Time series; Maximum likelihood (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:30:y:2015:i:1:p:131-150

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DOI: 10.1007/s00180-014-0526-x

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