Generalized hierarchical expected improvement method based on black-box functions of adaptive search strategy

DOI10.1016/J.APM.2021.12.041zbMath1503.90161OpenAlexW4206983367MaRDI QIDQ2109428

Yanyan Li

Publication date: 21 December 2022

Published in: Applied Mathematical Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.apm.2021.12.041

zbMATH Keywords

black-box functionadaptive search strategyanti-radiation testgeneralized hierarchical expected improvementhierarchical Gaussian process

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Probabilistic models, generic numerical methods in probability and statistics (65C20) Applications of mathematical programming (90C90) Derivative-free methods and methods using generalized derivatives (90C56) Approximation methods and heuristics in mathematical programming (90C59)

Uses Software

• SPACE
• EGO
• Spearmint
• Virtual library

Cites Work

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• Estimating the dimension of a model
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• The design and analysis of computer experiments
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• Machine-learning in optimization of expensive black-box functions
• An Efficient Surrogate Model for Emulation and Physics Extraction of Large Eddy Simulations
• Surrogate Optimization of Computationally Expensive Black-Box Problems with Hidden Constraints
• Convergence rates of efficient global optimization algorithms
• A central limit theorem for nested or sliced Latin hypercube designs
• A Theoretical Framework for Calibration in Computer Models: Parametrization, Estimation and Convergence Properties

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