Multi-Armed Bandit Regularized Expected Improvement for Efficient Global Optimization of Expensive Computer Experiments With Low Noise

Multi-Armed Bandit Regularized Expected Improvement for Efficient Global Optimization of Expensive Computer Experiments With Low Noise

Computer experiments are widely used to mimic expensive physical processes as black-box functions. A typical challenge of expensive computer experiments is to find the set of inputs that produce the desired response. This study proposes a multi-armed bandit regularized expected improvement (BREI) me...

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Bibliographic Details
Main Authors: Rajitha Meka, Adel Alaeddini, Chinonso Ovuegbe, Pranav A. Bhounsule, Peyman Najafirad, Kai Yang
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Computer experiments
Gaussian process regression
expected improvement
multi-armed bandit
Thompson sampling
Online Access:https://ieeexplore.ieee.org/document/9477602/

Internet

https://ieeexplore.ieee.org/document/9477602/

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