Abstract
State-of-the-art optimization algorithms often expose many parameters that should be configured to improve empirical performance. Manually tuning of such parameters is synonymous with tedious experiments which tend to lead to unsatisfactory outcomes. Accordingly, researchers developed several frameworks to tune the parameters of a given algorithm over a class of problems. Until very recently, however, these approaches are not testified and applied to many-objective algorithms. This study formulates a many-objective algorithm configuration (MAC) method which is available for the Matlab and Python. In MAC, we take into account the importance of a given configuration by building a conditional probability graph. In this light, the introduced algorithm aims to explore more important variables using an undirected fully-connected graph. Experimental results reveal that MAC performs better in comparison with state-of-the-art F-Race and SMAC frameworks.
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This research was supported in part through computational resources provided by Mésocentre of Strasbourg.
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Rakhshani, H., Idoumghar, L., Lepagnot, J., Brévilliers, M. (2019). MAC: Many-objective Automatic Algorithm Configuration. In: Deb, K., et al. Evolutionary Multi-Criterion Optimization. EMO 2019. Lecture Notes in Computer Science(), vol 11411. Springer, Cham. https://doi.org/10.1007/978-3-030-12598-1_20
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