Unbalanced budget distribution for automatic algorithm configuration

Optimization algorithms often have several critical setting parameters and the improvement of the empirical performance of these algorithms depends on tuning them. Manually configuration of such parameters is a tedious task that results in unsatisfactory outputs. Therefore, several automatic algorit...

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Veröffentlicht in:	Soft computing (Berlin, Germany) Jg. 26; H. 3; S. 1315 - 1330
Hauptverfasser:	Ghambari, Soheila, Rakhshani, Hojjat, Lepagnot, Julien, Jourdan, Laetitia, Idoumghar, Lhassane
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2022 Springer Verlag
Schlagworte:	Artificial Intelligence Computational Intelligence Computer Science Control Engineering Mathematical Logic and Foundations Mechatronics Operations Research Optimization Robotics Deep learning Automatic algorithm configuration Multi-objective optimization Bandit-based approaches
ISSN:	1432-7643, 1433-7479
Online-Zugang:	Volltext
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