[26] Tobias Friedrich, Karl Bringmann, Thomas Voß, and Christian Igel. The logarithmic hypervolume indicator. In Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms, pages 81-92, 2011.
[27] Jacob Gardner, Matt Kusner, Zhixiang, Kilian Weinberger, and John Cunningham. Bayesian optimization with inequality constraints. In Proceedings of the 31st International Conference on Machine Learning, volume 32 of Proceedings of Machine Learning Research, pages 937-945, Beijing, China, 22-24 Jun 2014. PMLR.
[28] Roman Garnett. Bayesian Optimization. Cambridge University Press, 2023. to appear.
[29] Michael A. Gelbart, Jasper Snoek, and Ryan P. Adams. Bayesian optimization with unknown constraints. In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, UAI, 2014.
[30] David Ginsbourger, Rodolphe Le Riche, and Laurent Carraro. A Multi-points Criterion for Deterministic Parallel Global Optimization based on Gaussian Processes. Technical report, March 2008. URL https://hal.science/hal-00260579.
[31] Robert B Gramacy, Annie Sauer, and Nathan Wycoff. Triangulation candidates for bayesian optimization. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, editors, Advances in Neural Information Processing Systems, volume 35, pages 35933-35945. Curran Associates, Inc., 2022.
[32] Nikolaus Hansen, Sibylle D. Müller, and Petros Koumoutsakos. Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (cma-es). Evolutionary Computation, 11(1):1-18, 2003. doi: 10.1162/106365603321828970.
[33] Tim Head, Manoj Kumar, Holger Nahrstaedt, Gilles Louppe, and Iaroslav Shcherbatyi. scikit-optimize/scikit-optimize, October 2021. URL https://doi.org/10.5281/zenodo.5565057.
[34] José Miguel Henrández-Lobato, Matthew W. Hoffman, and Zoubin Ghahramani. Predictive entropy search for efficient global optimization of black-box functions. In Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 1, NIPS'14, pages 918-926, Cambridge, MA, USA, 2014. MIT Press.
[35] Frank Hutter, Holger H Hoos, and Kevin Leyton-Brown. Sequential model-based optimization for general algorithm configuration. In Learning and Intelligent Optimization: 5th International Conference, LION 5, Rome, Italy, January 17-21, 2011. Selected Papers 5, pages 507-523. Springer, 2011.
[36] Carl Hvarfner, Frank Hutter, and Luigi Nardi. Joint entropy search for maximally-informed bayesian optimization. In Advances in Neural Information Processing Systems 35, 2022.
[37] Wolfram Research, Inc. Wolfram alpha, 2023. URL https://www.wolframalpha.com/.
[38] F. Irshad, S. Karsch, and A. Döpp. Multi-objective and multi-fidelity bayesian optimization of laser-plasma acceleration. Phys. Rev. Res., 5:013063, Jan 2023. doi: 10.1103/PhysRevResearch.5.013063. URL https://link.aps.org/doi/10.1103/PhysRevResearch.5.013063.
[39] Faran Irshad, Stefan Karsch, and Andreas Doepp. Reference dataset of multi-objective and multi- fidelity optimization in laser-plasma acceleration, January 2023. URL https://doi. org/10.5281/zenodo.7565882.
[40] Shali Jiang, Daniel Jiang, Maximilian Balandat, Brian Karrer, Jacob Gardner, and Roman Garnett. Efficient nonmyopic bayesian optimization via one-shot multi-step trees. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, pages 18039-18049. Curran Associates, Inc., 2020.
[41] Donald Jones, C. Perttunen, and B. Stuckman. Lipschitzian optimisation without the lipschitz constant. Journal of Optimization Theory and Applications, 79:157-181, 01 1993. doi: 10.1007/BF00941892.