Fetching the paper…
Reading the bibliography…
Surrogate algorithms such as Bayesian optimisation are especially designed for black-box optimisation problems with expensive objectives, such as hyperparameter tuning or simulation-based optimisation.
1907
Earlier work this paper cites.
R. Moriconi, M. P. Deisenroth, K. S. S. Kumar, High-dimensional bayesian optimization using low-dimensional feature spaces, Machine Learning 109 (2020) 1925–1943
1943
Earlier work this paper cites.
J. Močkus, On Bayesian methods for seeking the extremum, in: Optimization techniques IFIP technical conference, Springer, 1975, pp. 400–404
1975
Earlier work this paper cites.
S. Wagner, M. Affenzeller, HeuristicLab: A generic and extensible optimization environment, in: B. Ribeiro, R. F. Albrecht, A. Dobnikar, D. W. Pearson, N. C. Steele (Eds.), Adaptive and Natural Computing Algorithms, Springer Vienna, Vienna, 2005, pp. 538–541
2005
Earlier work this paper cites.
F. Hutter, H. H. Hoos, K. Leyton-Brown, Sequential model-based optimization for general algorithm configuration (extended version), Technical Report TR-2010–10, University of British Columbia, Computer Science, Tech. Rep. (2010)
2010
Earlier work this paper cites.
F. Hutter, H. H. Hoos, K. Leyton-Brown, Sequential model-based optimization for general algorithm configuration, in: International conference on learning and intelligent optimization, Springer, 2011, pp. 507–523
2011
Earlier work this paper cites.
K. van der Blom, T. M. Deist, V. Volz, M. Marchi, Y. Nojima, B. Naujoks, A. Oyama, T. Tušar, Identifying properties of real-world optimisation problems through a questionnaire, arXiv preprint arXiv:2011.05547 (2020) · 2011
Earlier work this paper cites.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, E. Duchesnay, Scikit-learn: Machine learning in Python, Journal of Machine Learning Research 12 (2011) 2825–2830
2011
Earlier work this paper cites.
J. M. Dieterich, B. Hartke, Empirical review of standard benchmark functions using evolutionary global optimization, arXiv preprint arXiv:1207.4318 (2012) · 2012
Earlier work this paper cites.
G. Ochoa, M. Hyde, T. Curtois, J. A. Vazquez-Rodriguez, J. Walker, M. Gendreau, G. Kendall, B. McCollum, A. J. Parkes, S. Petrovic, et al., HyFlex: A benchmark framework for cross-domain heuristic search, in: European Conference on Evolutionary Computation in Combinatorial Optimization, Springer, 2012, pp. 136–147
2012
Earlier work this paper cites.
J. Liu, Z.-H. Han, W. Song, Comparison of infill sampling criteria in kriging-based aerodynamic optimization, 28th Congress of the International Council of the Aeronautical Sciences 2012, ICAS 2012 2 (2012) 1625–1634
2012
Earlier work this paper cites.
J. Bergstra, Y. Bengio, Random search for hyper-parameter optimization, J. Mach. Learn. Res. 13 (2012) 281–305
2012
Earlier work this paper cites.
J. Bergstra, D. Yamins, D. Cox, Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures, in: International conference on machine learning, 2013, pp. 115–123
2013
Earlier work this paper cites.
J. Humeau, A. Liefooghe, E. Talbi, S. Vérel, ParadisEO-MO: from fitness landscape analysis to efficient local search algorithms, Journal of Heuristics 19 (2013) 881–915
2013
Earlier work this paper cites.
Z. hua Han, S. Görtz, R. Zimmermann, Improving variable-fidelity surrogate modeling via gradient-enhanced kriging and a generalized hybrid bridge function, Aerospace Science and Technology 25 (2013) 177–189
2013
Earlier work this paper cites.
doi:https://doi.org/10.1016/j.cor.2013.11.015
K. Smith-Miles, D. Baatar, B. Wreford, R. Lewis, Towards objective measures of algorithm performance across instance space, Computers & Operations Research 45 (2014) 12–24 · 2013
Earlier work this paper cites.
F. Nogueira, Bayesian Optimization: Open source constrained global optimization tool for Python (2014–). URL https://github.com/fmfn/BayesianOptimization
2014
Earlier work this paper cites.
J. Mueller, MATSuMoTo, https://github.com/Piiloblondie/MATSuMoTo (2014)
2014
Earlier work this paper cites.
B. Shahriari, K. Swersky, Z. Wang, R. Adams, N. D. Freitas, Taking the human out of the loop: A review of Bayesian optimization, Proceedings of the IEEE 104 (2016) 148–175
2016
Earlier work this paper cites.
K. Eggensperger, M. Feurer, A. Klein, S. Falkner, HPObench, https://github.com/automl/HPOBench (2016)
2016
Cited alongside, same era.
M. Lindauer, AClib2, https://bitbucket.org/mlindauer/aclib2 (2016)
2016
Cited alongside, same era.
T. Chen, C. Guestrin, XGBoost: A scalable tree boosting system, in: ACM SIGKDD, 2016, pp. 785–794
2016
Cited alongside, same era.
L. Bliek, H. R. G. W. Verstraete, M. Verhaegen, S. Wahls, Online optimization with costly and noisy measurements using random Fourier expansions, IEEE Transactions on Neural Networks and Learning Systems 29 (1) (2018) 167–182
2018
Cited alongside, same era.
A. Bhosekar, M. G. Ierapetritou, Advances in surrogate based modeling, feasibility analysis, and optimization: A review, Comput. Chem. Eng. 108 (2018) 250–267
2018
Cited alongside, same era.
V. Volz, B. Naujoks, P. Kerschke, T. Tusar, Single- and multi-objective game-benchmark for evolutionary algorithms, Proceedings of the Genetic and Evolutionary Computation Conference (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
C. Oh, J. Tomczak, E. Gavves, M. Welling, Combinatorial bayesian optimization using the graph cartesian product , in: Advances in Neural Information Processing Systems, Vol. 32, 2019, pp. 1–11. URL https://proceedings.neurips.cc/paper/2019/file/2cb6b10338a7fc4117a80da24b582060-Paper.pdf
2019
Later among the works it cites.
F. Bre, N. D. Roman, V. D. Fachinotti, An efficient metamodel-based method to carry out multi-objective building performance optimizations, Energy and Buildings 206 (2020) 109576
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
S. J. Daniels, A. A. Rahat, R. M. Everson, G. R. Tabor, J. E. Fieldsend, A suite of computationally expensive shape optimisation problems using computational fluid dynamics, in: International Conference on Parallel Problem Solving from Nature, Springer, 2018, pp. 296–307
2018
Cited alongside, same era.
C. Doerr, H. Wang, F. Ye, S. van Rijn, T. Bäck, IOHprofiler: A benchmarking and profiling tool for iterative optimization heuristics , arXiv e-prints:1810.05281 (Oct. 2018) · 2018
Cited alongside, same era.
S. Falkner, A. Klein, F. Hutter, BOHB: Robust and efficient hyperparameter optimization at scale, in: Proceedings of the 35th International Conference on Machine Learning, 2018, pp. 1436–1445
2018
Cited alongside, same era.
A. Costa, G. Nannicini, RBFOpt: an open-source library for black-box optimization with costly function evaluations, Mathematical Programming Computation 10 (2018) 597–629
2018
Cited alongside, same era.
J. Rapin, O. Teytaud, Nevergrad - A gradient-free optimization platform, https://GitHub.com/FacebookResearch/Nevergrad (2018)
2018
Cited alongside, same era.
R. Turner, D. Eriksson, BayesMark, https://github.com/uber/bayesmark (2018)
2018
Cited alongside, same era.
F. Rehbach, M. Zaefferer, J. Stork, T. Bartz-Beielstein, Comparison of parallel surrogate-assisted optimization approaches, in: Proceedings of the Genetic and Evolutionary Computation Conference, 2018, pp. 1348–1355
2018
Cited alongside, same era.
A. J. Keane, I. I. Voutchkov, Surrogate approaches for aerodynamic section performance modeling, AIAA Journal 58 (2020) 16–24
2020
Later among the works it cites.
R. Alizadeh, J. K. Allen, F. Mistree, Managing computational complexity using surrogate models: a critical review, Research in Engineering Design 31 (2020) 275–298
2020
Later among the works it cites.
L. Bliek, S. Verwer, M. de Weerdt, Black-box combinatorial optimization using models with integer-valued minima, Annals of Mathematics and Artificial Intelligence (2020) 1–15 doi:https://doi.org/10.1007/s10472-020-09712-4
2020
Later among the works it cites.
B. Ru, A. Alvi, V. Nguyen, M. A. Osborne, S. Roberts, Bayesian optimisation over multiple continuous and categorical inputs, in: H. D. III, A. Singh (Eds.), Proceedings of the 37th International Conference on Machine Learning, Vol. 119 of Proceedings of Machine Learning Research, PMLR, 2020, pp. 8276–8285
2020
Later among the works it cites.
doi:10.3390/math8050785
F. Caraffini, G. Iacca, The SOS platform: Designing, tuning and statistically benchmarking optimisation algorithms , Mathematics 8 (5) (2020) · 2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
T. Pourmohamad, CompModels: A suite of computer model test functions for Bayesian optimization, arXiv: Computation (2020)
2020
Later among the works it cites.
NREL, FLORIS. Version 2.1.1 (2020). URL https://github.com/NREL/floris
2020
Later among the works it cites.
R. Karlsson, L. Bliek, S. Verwer, M. de Weerdt, Continuous surrogate-based optimization algorithms are well-suited for expensive discrete problems, in: Proceedings of the Benelux Conference on Artificial Intelligence, 2020, pp. 88–102
2020
Later among the works it cites.
Q. Liang, A. E. Gongora, Z. Ren, A. Tiihonen, Z. Liu, S. Sun, J. R. Deneault, D. Bash, F. Mekki-Berrada, S. A. Khan, K. Hippalgaonkar, B. Maruyama, K. A. Brown, J. W. F. Iii, T. Buonassisi, Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains, npj Computational Materials 7 (2021) 1–10
2021
Closest in time.
doi:10.1145/3449726.3463136
L. Bliek, A. Guijt, S. Verwer, M. de Weerdt, Black-box mixed-variable optimisation using a surrogate model that satisfies integer constraints , in: Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO ’21, Association for Computing Machinery, New York, NY, USA, 2021, p. 1851–1859 · 2021
Closest in time.
T. Eimer, A. Biedenkapp, M. Reimer, S. Adriaensen, F. Hutter, M. Lindauer, DACBench: A benchmark library for dynamic algorithm configuration, in: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI’21), ijcai.org, 2021, pp. 1668–1674
2021
Closest in time.
D. Gorissen, I. Couckuyt, P. Demeester, T. Dhaene, K. Crombecq, A surrogate modeling and adaptive sampling toolbox for computer based design, J. Mach. Learn. Res. 11 (2010) 2051–2055
2055
Closest in time.