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Efficient Global Optimization of Expensive Black-Box Functions
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“These non-linear models encompass non-parametric (KNN), kernel-based (KRR, SVR and GP) and tree-ensemble (RFR, XGB and ETR) approaches, enabling a comprehensive comparison34,35,47.”
From Data-driven de novo design of super-adhesive hydrogels (2025) · cites this paper as [47]
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Taking the Human Out of the Loop: A Review of Bayesian Optimization
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Metadata
- Authors
- Jones, D. R., Schonlau, M., & Welch, W. J.
- Affiliations
- General Motors (United States) (US)
- National Institute of Statistical Sciences (US)
- University of Waterloo (CA)
- Venue
- Journal of Global Optimization
- Published
- 1998-12
- Volume
- 13
- Issue
- 4
- Pages
- 455-492
- ISSN
- 0925-5001 (ISSN-L)
- 1573-2916
- OpenAlex
- W1510052597
- Cited by
- 8,070
- References
- 34
- Citations by year
- 2026: 730 · 2025: 661 · 2024: 601 · 2023: 599 · 2022: 636 · 2021: 659 · 2020: 612 · 2019: 548 · 2018: 458 · 2017: 369 · 2016: 370 · 2015: 284 · 2014: 255 · 2013: 219 · 2012: 215
- FWCI
- 7.84
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Global Optimization (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Numerical Analysis › Advanced Optimization Algorithms Research
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Advanced Multi-Objective Optimization Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Keywords
- expensive black-box functions, global optimization, response surface methodology, surrogate models, stopping rules, exploration-exploitation balance, prediction error, engineering design optimization
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