Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques
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Publication:2182781
DOI10.1007/s11590-019-01428-7zbMath1444.90119OpenAlexW2944120742WikidataQ127901328 ScholiaQ127901328MaRDI QIDQ2182781
Publication date: 26 May 2020
Published in: Optimization Letters (Search for Journal in Brave)
Full work available at URL: https://www.osti.gov/biblio/1642435
machine learningblack-box optimizationsurrogate modelingdata-driven optimizationsubset selection for regression
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Initialization of metaheuristics: comprehensive review, critical analysis, and research directions ⋮ Surrogate-based branch-and-bound algorithms for simulation-based black-box optimization ⋮ Physics-informed deep learning for simultaneous surrogate modeling and PDE-constrained optimization of an airfoil geometry ⋮ Adaptive approximation-based multi-objective hybrid optimization method for dual-gradient top-hat structures ⋮ A subset-selection-based derivative-free optimization algorithm for dynamic operation optimization in a steel-making process ⋮ Adaptive surrogate-based harmony search algorithm for design optimization of variable stiffness composite materials ⋮ Computational design of innovative mechanical metafilters via adaptive surrogate-based optimization
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