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An analysis of the Taguchi method for tuning a memetic algorithm with reduced computational time budget

Gümüş, Düriye Betül; Özcan, Ender; Atkin, Jason

Authors

Düriye Betül Gümüş



Abstract

Determining the best initial parameter values for an algorithm, called parameter tuning, is crucial to obtaining better algorithm performance; however, it is often a time-consuming task and needs to be performed under a restricted computational budget. In this study, the results from our previous work on using the Taguchi method to tune the parameters of a memetic algorithm for cross-domain search are further analysed and extended. Although the Taguchi method reduces the time spent finding a good parameter value combination by running a smaller size of experiments on the training instances from different domains as opposed to evaluating all combinations, the time budget is still larger than desired. This work investigates the degree to which it is possible to predict the same good parameter setting faster by using a reduced time budget. The results in this paper show that it was possible to predict good combinations of parameter settings with a much reduced time budget. The good final parameter values are predicted for three of the parameters, while for the fourth parameter there is no clear best value, so one of three similarly performing values is identified at each time instant.

Citation

Gümüş, D. B., Özcan, E., & Atkin, J. (2016). An analysis of the Taguchi method for tuning a memetic algorithm with reduced computational time budget. In Computer and information sciences: 31st International Symposium, ISCIS 2016, Kraków, Poland, October 27–28, 2016, proceedings. Springer. https://doi.org/10.1007/978-3-319-47217-1_2

Acceptance Date Jul 13, 2016
Publication Date Sep 24, 2016
Deposit Date Oct 4, 2016
Publicly Available Date Oct 4, 2016
Electronic ISSN 1865-0929
Peer Reviewed Peer Reviewed
Volume 659
Issue 659
Series Title Communications in computer and information science
Book Title Computer and information sciences: 31st International Symposium, ISCIS 2016, Kraków, Poland, October 27–28, 2016, proceedings
ISBN 978-3-319-47217-1
DOI https://doi.org/10.1007/978-3-319-47217-1_2
Keywords Evolutionary algorithm; Parameter tuning; Design of experiments; Hyper-heuristic; Optimisation
Public URL http://eprints.nottingham.ac.uk/id/eprint/37338
Publisher URL http://link.springer.com/chapter/10.1007%2F978-3-319-47217-1_2
Copyright Statement Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0





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