Statistical optimisation and tuning of GA factors.

Petrovski, A.; Brownlee, A.; McCall, J.

Abstract

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.

Citation

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: https://doi.org/10.1109/CEC.2005.1554759

• 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: IEEE Institute of Electrical and Electronics Engineers
• Article Number: 1554759
• Pages: 758-764
• Series Title: IEEE transactions on evolutionary computation
• ISBN: 0780393635
• DOI: https://doi.org/10.1109/CEC.2005.1554759
• Keywords: Genetic algorithms; Algorithm design and analysis; Performance analysis; Analysis of variance; Space exploration; Processor scheduling; Computer science; Application software; Encoding; Genetic engineering
• Public URL: http://hdl.handle.net/10059/433