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

G�m�?, D�riye Bet�l; �zcan, Ender; Atkin, Jason

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, October). An analysis of the Taguchi method for tuning a memetic algorithm with reduced computational time budget. Presented at ISCIS: International Symposium on Computer and Information Sciences, Krakow, Poland

• Presentation Conference Type: Edited Proceedings
• Conference Name: ISCIS: International Symposium on Computer and Information Sciences
• Start Date: Oct 27, 2016
• End Date: Oct 28, 2016
• Acceptance Date: Jul 13, 2016
• Online Publication Date: Sep 23, 2016
• Publication Date: Sep 24, 2016
• Deposit Date: Oct 4, 2016
• Publicly Available Date: Oct 4, 2016
• Electronic ISSN: 1865-0929
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 659
• Issue: 659
• Pages: 12–20
• Series Title: Communications in computer and information science
• Series ISSN: 1865-0937
• Book Title: Computer and information sciences: 31st International Symposium, ISCIS 2016, Kraków, Poland, October 27–28, 2016, proceedings
• ISBN: 978-3-319-47216-4
• 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: https://nottingham-repository.worktribe.com/output/809532
• Publisher URL: http://link.springer.com/chapter/10.1007%2F978-3-319-47217-1_2
• Contract Date: Oct 4, 2016