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Statistical optimisation and tuning of GA factors.

Petrovski, Andrei; Brownlee, Alexander; McCall, John


Alexander Brownlee


This paper presents a practical methodology of improving the efficiency of Genetic Algorithms through tuning the factors significantly affecting GA performance. This methodology is based on the methods of statistical inference and has been successfully applied to both binary- and integer-encoded Genetic Algorithms that search for good chemotherapeutic schedules.


PETROVSKI, A., BROWNLEE, A. and MCCALL, J. 2005. Statistical optimisation and tuning of GA factors. In Proceedings of the 2005 IEEE congress on evolutionary computation (CEC 2005), 2-5 September 2005, Edinburgh, UK. New York: IEEE [online], volume 1, article number 1554759, pages 758-764. Available from:

Conference Name 2005 IEEE congress on evolutionary computation (CEC 2005)
Conference Location Edinburgh, UK
Start Date Sep 2, 2005
End Date Sep 5, 2005
Acceptance Date Sep 30, 2005
Online Publication Date Sep 30, 2005
Publication Date Dec 31, 2005
Deposit Date Oct 20, 2009
Publicly Available Date Oct 20, 2009
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Article Number 1554759
Pages 758-764
Series Title IEEE transactions on evolutionary computation
ISBN 0780393635
Keywords Genetic algorithms; Algorithm design and analysis; Performance analysis; Analysis of variance; Space exploration; Processor scheduling; Computer science; Application software; Encoding; Genetic engineering
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