Using the knowledge gradient acquisition function in Bayesian optimization when searching for robust solutions-Reference-Cited by-同舟云学术

Using the knowledge gradient acquisition function in Bayesian optimization when searching for robust solutions

Published:2022-12-05 Issue: Volume: Page:1-18
ISSN:0305-215X
Container-title:Engineering Optimization
language:en
Short-container-title:Engineering Optimization

Author:

Phuong Le Hoai¹,Branke Juergen²

Affiliation:

1. Mathematics for Real-World Systems, University of Warwick, Coventry, UK

2. Warwick Business School, University of Warwick, Coventry, UK

Funder

EPSRC

Publisher

Informa UK Limited

Subject

Applied Mathematics,Industrial and Manufacturing Engineering,Management Science and Operations Research,Control and Optimization,Computer Science Applications

Link

https://www.tandfonline.com/doi/pdf/10.1080/0305215X.2022.2145604

Reference31 articles.

1. Abadi, Martin, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, et al. 2016. “TensorFlow: A System for Large-Scale Machine Learning.” In Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI'16), 265–283. Berkeley, CA: USENIX Association.

2. Objective-Oriented Sequential Sampling for Simulation Based Robust Design Considering Multiple Sources of Uncertainty

3. Robust optimization – A comprehensive survey

4. Branke, Juergen. 1998. “Creating Robust Solutions by Means of Evolutionary Algorithms.” In Proceedings of Parallel Problem Solving from Nature (PPSN V), edited by Agoston E. Eiben, Thomas Bäck, Marc Schoenauer, and Hans-Paul Schwefel, 119–128. Berlin: Springer.

5. Frazier, Peter. 2018. “A Tutorial on Bayesian Optimization.” https://arxiv.org/abs/1807.0281.

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