A Regression Tree as Acquisition Function for Low-Dimensional Optimisation-Reference-Cited by-同舟云学术

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A Regression Tree as Acquisition Function for Low-Dimensional Optimisation

Published:2024 Issue: Volume: Page:23-33
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:en
Short-container-title:

Author:

de Paz Erick G. G.^ORCID,Vaquera Huerta Humberto^ORCID,Albores Velasco Francisco Javier^ORCID,Bauer Mengelberg John R.^ORCID,Romero Padilla Juan Manuel^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-62836-8_3

Reference19 articles.

1. Archetti, F., Candelieri, A.: Bayesian Optimization and Data Science. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-24494-1

2. Bäck, T.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press, Oxford (1996). https://doi.org/10.1093/oso/9780195099713.001.0001

3. De Ath, G., Everson, R.M., Rahat, A.A.M., Fieldsend, J.E.: Greed is good: exploration and exploitation trade-offs in bayesian optimisation. ACM Trans. Evol. Learn. Optim. 1(1), 1–22 (2021). https://doi.org/10.1145/3425501

4. Garnett, R.: Bayesian Optimization. Cambridge University Press, Cambridge (2023)

5. Glasserman, P.: Monte Carlo Methods in Financial Engineering. Applications of Mathematics: Stochastic Modelling and Applied Probability. Springer, New York (2004). https://doi.org/10.1007/978-0-387-21617-1

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1. CPoptim: Convex Partition Optimisation;CRAN: Contributed Packages;2024-07-12

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