Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/34421
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: A systematic approach to parameter optimization and its application to flight schedule simulation software
Author(s): Brownlee, Alexander E I
Epitropakis, Michael G
Mulder, Jeroen
Paelinck, Marc
Burke, Edmund K
Contact Email: alexander.brownlee@stir.ac.uk
Keywords: Parameter tuning
optimization
statistical methods
multi-objective optimization
search-based software engineering
explanation
Issue Date: 5-Jul-2022
Date Deposited: 17-Jun-2022
Citation: Brownlee AEI, Epitropakis MG, Mulder J, Paelinck M & Burke EK (2022) A systematic approach to parameter optimization and its application to flight schedule simulation software. Journal of Heuristics. https://doi.org/10.1007/s10732-022-09501-8
Abstract: Industrial software often has many parameters that critically impact performance. Frequently, these are left in a sub-optimal configuration for a given application because searching over possible configurations is costly and, except for developer instinct, the relationships between parameters and performance are often unclear and complex. While there have been significant advances in automated parameter tuning approaches recently, they are typically black-box. The high-quality solutions produced are returned to the user without explanation. The nature of optimisation means that, often, these solutions are far outside the well-established settings for the software, making it difficult to accept and use them. To address the above issue, a systematic approach to software parameter optimization is presented. Several well-established techniques are followed in sequence, each underpinning the next, with rigorous analysis of the search space. This allows the results to be explainable to both end users and developers , improving confidence in the optimal solutions, particularly where they are counter-intuitive. The process comprises statistical analysis of the parameters; single-objective optimization for each target objective; functional ANOVA to explain trends and inter-parameter interactions; and a multi-objective optimization seeded with the results from the single-objective stage. A case study demonstrates application to business-critical software developed by the international airline Air France-KLM for measuring flight schedule robustness. A. 2 Alexander E. I. Brownlee et al. configuration is found with a run-time of 80% that of the tried-and-tested configuration, with no loss in predictive accuracy. The configuration is supplemented with detailed analysis explaining the importance of each parameter, how they interact with each other, how they influence run-time and accuracy , and how the final configuration was reached. In particular, this explains why the configuration included some parameter settings that were outwith the usually recommended range, greatly increasing developer confidence and encouraging adoption of the new configuration.
DOI Link: 10.1007/s10732-022-09501-8
Rights: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Notes: Output Status: Forthcoming/Available Online
Licence URL(s): http://creativecommons.org/licenses/by/4.0/

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