Comparison of Bayesian Optimization Algorithms for BBOB Problems in Dimensions 10 and 60

Author:

Santoni Maria Laura1ORCID,Raponi Elena21ORCID,De Leone Renato3ORCID,Doerr Carola14ORCID

Affiliation:

1. Sorbonne Université, Paris, France

2. Technical University of Munich, Munich, Germany

3. University of Camerino, Camerino, Italy

4. CNRS, Paris, France

Funder

Centre National de la Recherche Scientifique

Agence Nationale de la Recherche

German Academic Exchange Service (DAAD)

Sorbonne Université

Publisher

ACM

Reference13 articles.

1. High Dimensional Bayesian Optimization with Kernel Principal Component Analysis

2. A Survey on High-dimensional Gaussian Process Modeling with Application to Bayesian Optimization

3. Carola Doerr , Hao Wang , Furong Ye , Sander van Rijn , and Thomas Bäck . 2018. IOHprofiler: A benchmarking and profiling tool for iterative optimization heuristics. arXiv preprint arXiv:1810.05281 ( 2018 ). https://iohprofiler.github.io/. Carola Doerr, Hao Wang, Furong Ye, Sander van Rijn, and Thomas Bäck. 2018. IOHprofiler: A benchmarking and profiling tool for iterative optimization heuristics. arXiv preprint arXiv:1810.05281 (2018). https://iohprofiler.github.io/.

4. David Eriksson and Martin Jankowiak. 2021. High-dimensional Bayesian optimization with sparse axis-aligned subspaces. In Uncertainty in Artificial Intelligence. PMLR 493--503. David Eriksson and Martin Jankowiak. 2021. High-dimensional Bayesian optimization with sparse axis-aligned subspaces. In Uncertainty in Artificial Intelligence. PMLR 493--503.

5. David Eriksson , Michael Pearce , Jacob Gardner , Ryan D Turner , and Matthias Poloczek . 2019. Scalable global optimization via local Bayesian Optimization. Advances in Neural Information Processing Systems 32 ( 2019 ). David Eriksson, Michael Pearce, Jacob Gardner, Ryan D Turner, and Matthias Poloczek. 2019. Scalable global optimization via local Bayesian Optimization. Advances in Neural Information Processing Systems 32 (2019).

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