Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques
Author:
Funder
Georgia Institute of Technology
U.S. Department of Energy
Publisher
Springer Science and Business Media LLC
Subject
Control and Optimization
Link
http://link.springer.com/content/pdf/10.1007/s11590-019-01428-7.pdf
Reference56 articles.
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4. Tekin, E., Sabuncuoglu, I.: Simulation optimization: a comprehensive review on theory and applications. IIE Trans. 36(11), 1067–1081 (2004)
5. Bhosekar, A., Ierapetritou, M.: Advances in surrogate based modeling, feasibility analysis, and optimization: a review. Comput. Chem. Eng. 108, 250–267 (2018)
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