Parameter Tuning Problem in Metaheuristics: A Self-Adaptive Local Search Algorithm for Combinatorial Problems

Alabaş-Uslu, Çiğdem; Dengız, Berna

doi:10.1007/978-3-030-11866-2_5

Cited by 1 publication

(1 citation statement)

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“…Automated parameter tuning can be broadly divided into offline tuning, where we tune then fix the parameters and run the algorithm (Eiben and Smit 2011;Hutter 2009;Birattari and Kacprzyk 2009;Stützle and López-Ibáñez 2019) and online parameter control (Karafotias et al 2015;Alabas-Uslu and Dengiz 2020) where the parameters are tuned during the run. The framework proposed in the present paper focuses on offline tuning.…”

Section: Automated Parameter Tuning and Software Improvementmentioning

confidence: 99%

A systematic approach to parameter optimization and its application to flight schedule simulation software

Brownlee

Epitropakis²,

Mulder

et al. 2022

J Heuristics

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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 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.

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