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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Vydané v:	Soft computing (Berlin, Germany) Ročník 26; číslo 3; s. 1315 - 1330
Hlavní autori:	Ghambari, Soheila, Rakhshani, Hojjat, Lepagnot, Julien, Jourdan, Laetitia, Idoumghar, Lhassane
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2022 Springer Verlag
Predmet:	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
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