An improved high-dimensional Bayesian optimization algorithm

The Bayesian Optimization Algorithm, as an effective approach to addressing non-linear global optimization problems, is widely embraced in a myriad of machine learning application domains. With the development of big data, the presence of computational and statistical challenges in high-dimensional...

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Vydané v:	Applied intelligence (Dordrecht, Netherlands) Ročník 55; číslo 13; s. 915
Hlavní autori:	Guan, Juan, Wang, Yanhua
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.08.2025 Springer Nature B.V
Predmet:	Algorithms Artificial Intelligence Bayesian analysis Clustering Computer Science Datasets Decomposition Design optimization Global optimization Machine learning Machines Manufacturing Mechanical Engineering Methods Optimization algorithms Principal components analysis Processes KPCA High-dimensional Bayesian optimization MIMIC algorithm Variable interaction
ISSN:	0924-669X, 1573-7497
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Abstract	The Bayesian Optimization Algorithm, as an effective approach to addressing non-linear global optimization problems, is widely embraced in a myriad of machine learning application domains. With the development of big data, the presence of computational and statistical challenges in high-dimensional settings means that, despite the proposed improvements and enhancements, the applicability of the Bayesian Optimization Algorithm is still restricted to low-dimensional problems. Our algorithm (1) extracts an interesting nonlinear latent structure in the function by Kernal Principal Component Analysis(KPCA) to reduce the computational complexity, and (2) uses an improved Mutual-Information-Maximizing Input Clustering (MIMIC) algorithm to optimize only a low-dimensional subspace each iteration for more efficient and effective BO. The experiments demonstrate that the proposed algorithm can achieve a clear improvement in optimization accuracy and speed in high-dimensional space and can efficiently solve high-dimensional problems for Bayesian optimization algorithm.
AbstractList	The Bayesian Optimization Algorithm, as an effective approach to addressing non-linear global optimization problems, is widely embraced in a myriad of machine learning application domains. With the development of big data, the presence of computational and statistical challenges in high-dimensional settings means that, despite the proposed improvements and enhancements, the applicability of the Bayesian Optimization Algorithm is still restricted to low-dimensional problems. Our algorithm (1) extracts an interesting nonlinear latent structure in the function by Kernal Principal Component Analysis(KPCA) to reduce the computational complexity, and (2) uses an improved Mutual-Information-Maximizing Input Clustering (MIMIC) algorithm to optimize only a low-dimensional subspace each iteration for more efficient and effective BO. The experiments demonstrate that the proposed algorithm can achieve a clear improvement in optimization accuracy and speed in high-dimensional space and can efficiently solve high-dimensional problems for Bayesian optimization algorithm.
ArticleNumber	915
Author	Wang, Yanhua Guan, Juan
Author_xml	– sequence: 1 givenname: Juan surname: Guan fullname: Guan, Juan email: guanjuan@163.sufe.edu.cn organization: School of Mathematics, Shanghai University of Finance and Economics – sequence: 2 givenname: Yanhua surname: Wang fullname: Wang, Yanhua organization: School of Mathematics, Shanghai University of Finance and Economics, Shanghai Key Laboratory of Financial Information Technology, Shanghai University of Finance and Economics
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Cites_doi	10.1016/j.tust.2020.103493 10.1214/aoms/1177704248 10.1007/978-3-642-15871-1_31 10.1016/B978-0-444-63428-3.50180-6 10.1007/BF00941892 10.1145/2939672.2945367 10.1007/978-3-319-23871-5_3 10.1021/acs.jcim.1c00637 10.1080/01621459.1937.10503522 10.1142/S0129065704001899 10.1145/3582078 10.1145/2858036.2858253 10.24963/ijcai.2019/596 10.1016/j.swevo.2021.100888 10.1007/s10287-020-00376-3 10.1038/s43586-022-00184-w 10.1007/s10994-021-06019-1 10.1007/BFb0020217
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SubjectTerms	Algorithms Artificial Intelligence Bayesian analysis Clustering Computer Science Datasets Decomposition Design optimization Global optimization Machine learning Machines Manufacturing Mechanical Engineering Methods Optimization algorithms Principal components analysis Processes
Title	An improved high-dimensional Bayesian optimization algorithm
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