A tutorial on Bayesian optimization
PI Frazier - arxiv preprint arxiv:1807.02811, 2018 - arxiv.org
Bayesian optimization is an approach to optimizing objective functions that take a long time
(minutes or hours) to evaluate. It is best-suited for optimization over continuous domains of …
(minutes or hours) to evaluate. It is best-suited for optimization over continuous domains of …
Parallel surrogate-assisted global optimization with expensive functions–a survey
Surrogate assisted global optimization is gaining popularity. Similarly, modern advances in
computing power increasingly rely on parallelization rather than faster processors. This …
computing power increasingly rely on parallelization rather than faster processors. This …
Bayesian optimization
PI Frazier - Recent advances in optimization and modeling …, 2018 - pubsonline.informs.org
Bayesian optimization is an approach to optimizing objective functions that take a long time
(minutes or hours) to evaluate. It is best suited for optimization over continuous domains of …
(minutes or hours) to evaluate. It is best suited for optimization over continuous domains of …
Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions
S Shan, GG Wang - Structural and multidisciplinary optimization, 2010 - Springer
The integration of optimization methodologies with computational analyses/simulations has
a profound impact on the product design. Such integration, however, faces multiple …
a profound impact on the product design. Such integration, however, faces multiple …
Global optimization advances in mixed-integer nonlinear programming, MINLP, and constrained derivative-free optimization, CDFO
This manuscript reviews recent advances in deterministic global optimization for Mixed-
Integer Nonlinear Programming (MINLP), as well as Constrained Derivative-Free …
Integer Nonlinear Programming (MINLP), as well as Constrained Derivative-Free …
Progressive Latin Hypercube Sampling: An efficient approach for robust sampling-based analysis of environmental models
Efficient sampling strategies that scale with the size of the problem, computational budget,
and users' needs are essential for various sampling-based analyses, such as sensitivity and …
and users' needs are essential for various sampling-based analyses, such as sensitivity and …
Surrogate‐based methods for black‐box optimization
In this paper, we survey methods that are currently used in black‐box optimization, that is,
the kind of problems whose objective functions are very expensive to evaluate and no …
the kind of problems whose objective functions are very expensive to evaluate and no …
Influence of ensemble surrogate models and sampling strategy on the solution quality of algorithms for computationally expensive black-box global optimization …
This paper examines the influence of two major aspects on the solution quality of surrogate
model algorithms for computationally expensive black-box global optimization problems …
model algorithms for computationally expensive black-box global optimization problems …
Global optimization of general constrained grey-box models: new method and its application to constrained PDEs for pressure swing adsorption
This paper introduces a novel methodology for the global optimization of general
constrained grey-box problems. A grey-box problem may contain a combination of black-box …
constrained grey-box problems. A grey-box problem may contain a combination of black-box …
Pseudo expected improvement criterion for parallel EGO algorithm
D Zhan, J Qian, Y Cheng - Journal of Global Optimization, 2017 - Springer
The efficient global optimization (EGO) algorithm is famous for its high efficiency in solving
computationally expensive optimization problems. However, the expected improvement (EI) …
computationally expensive optimization problems. However, the expected improvement (EI) …