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Surrogate-based optimization relies on so-called infill criteria (acquisition functions) to decide which point to evaluate next.
No-PASt-BO: Normalized Portfolio Allocation Strategy for Bayesian Optimization
Thiago de P. Vasconcelos, Daniel A. R. M. A. de Souza, César L. C. Mattos, and João P. P. Gomes. [n. d.] · 1908
Earlier work this paper cites.
Greed is Good: Exploration and Exploitation Trade-offs in Bayesian Optimisation
George De Ath, Richard M. Everson, Alma A. M. Rahat, and Jonathan E. Fieldsend. 2019 · 1911
Earlier work this paper cites.
Comparison of three methods for selecting values of input variables in the analysis of output from a computer code
Michael D McKay, Richard J Beckman, and William J Conover. 1979 · 1979
Earlier work this paper cites.
Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces
Rainer Storn and Kenneth Price. 1997 · 1997
Earlier work this paper cites.
Efficient global optimization of expensive black-box functions
Donald R. Jones, Matthias Schonlau, and William J. Welch. 1998 · 1998
Earlier work this paper cites.
Using confidence bounds for exploitation-exploration trade-offs
Peter Auer. 2002 · 2002
Earlier work this paper cites.
Sequential parameter optimization. In 2005 IEEE Congress on Evolutionary Computation (CEC 2005)
T. Bartz-Beielstein, C.W.G. Lasarczyk, and M. Preuss. 2005 · 2005
Earlier work this paper cites.
Engineering Design via Surrogate Modelling
Alexander Forrester, Andras Sobester, and Andy Keane. 2008 · 2008
Earlier work this paper cites.
Real-parameter black-box optimization benchmarking 2009: Noiseless functions definitions
Nikolaus Hansen, Steffen Finck, Raymond Ros, and Anne Auger. 2009 · 2009
Earlier work this paper cites.
Towards Gaussian Process-based Optimization with Finite Time Horizon. In mODa 9 – Advances in Model-Oriented Design and Analysis
David Ginsbourger and Rodolphe Le Riche. 2010 · 2010
Earlier work this paper cites.
Bayesian Gaussian processes for sequential prediction, optimisation and quadrature
Michael Osborne. 2010 · 2010
Earlier work this paper cites.
Convergence Rates of Efficient Global Optimization Algorithms
Adam D. Bull. 2011 · 2011
Cited alongside, same era.
A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms
Joaquín Derrac, Salvador García, Daniel Molina, and Francisco Herrera. 2011 · 2011
Cited alongside, same era.
Portfolio Allocation for Bayesian Optimization. In Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence
Matthew Hoffman, Eric Brochu, and Nando de Freitas. 2011 · 2011
Cited alongside, same era.
DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization
Olivier Roustant, David Ginsbourger, and Yves Deville. 2012 · 2012
Cited alongside, same era.
Practical Bayesian Optimization of Machine Learning Algorithms. In Advances in Neural Information Processing Systems 25 (NIPS 2012)
Jasper Snoek, Hugo Larochelle, and Ryan P Adams. 2012 · 2012
mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions
Bernd Bischl, Jakob Richter, Jakob Bossek, Daniel Horn, Janek Thomas, and Michel Lang. 2017 · 2017
Later among the works it cites.
smoof: Single- and Multi-Objective Optimization Test Functions
Jakob Bossek. 2017 · 2017
Later among the works it cites.
Time complexity reduction in efficient global optimization using cluster Kriging. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’17)
Hao Wang, Bas van Stein, Michael Emmerich, and Thomas Bäck. 2017 · 2017
Later among the works it cites.
The true destination of EGO is multi-local optimization. In 2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI)
Simon Wessing and Mike Preuss. 2017 · 2017
Later among the works it cites.
On a New Improvement-Based Acquisition Function for Bayesian Optimization
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Cited alongside, same era.
Predictive Entropy Search for Efficient Global Optimization of Black-box Functions. In Proceedings of the 27th International Conference on Neural Information Processing Systems
José Miguel Hernández-Lobato, Matthew W. Hoffman, and Zoubin Ghahramani. 2014 · 2014
Cited alongside, same era.
Nonparametric Statistical Methods
Myles Hollander, Douglas A. Wolfe, and Eric Chicken. 2014 · 2014
Cited alongside, same era.
Optimally Weighted Cluster Kriging for Big Data Regression. In Advances in Intelligent Data Analysis XIV, 14th International Symposium, IDA 2015
Bas van Stein, Hao Wang, Wojtek Kowalczyk, Thomas Bäck, and Michael Emmerich. 2015 · 2015
Cited alongside, same era.
COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting
Nikolaus Hansen, Anne Auger, Olaf Mersmann, Tea Tusar, and Dimo Brockhoff. 2016 · 2016
Cited alongside, same era.
Bayesian Optimization with a Finite Budget: An Approximate Dynamic Programming Approach. In Advances in Neural Information Processing Systems 29 (NIPS 2016)
Remi Lam, Karen Willcox, and David H. Wolpert. 2016 · 2016
Cited alongside, same era.
Budgeted Batch Bayesian Optimization. In 2016 IEEE 16th International Conference on Data Mining (ICDM)
Vu Nguyen, Santu Rana, Sunil K Gupta, Cheng Li, and Svetha Venkatesh. 2016 · 2016
Cited alongside, same era.
Umberto Noé and Dirk Husmeier. 2018 · 2018
Later among the works it cites.
Cooling Strategies for the Moment-Generating Function in Bayesian Global Optimization. In 2018 IEEE Congress on Evolutionary Computation (CEC)
Hao Wang, Michael Emmerich, and Thomas Back. 2018 · 2018
Later among the works it cites.
SPOT: Sequential Parameter Optimization Toolbox – Version 2.0.4
Thomas Bartz-Beielstein, Joerg Stork, Martin Zaefferer, Margarita Rebolledo, Christian Lasarczyk, Joerg Ziegenhirt, Wolfgang Konen, Oliver Flasch, Patrick Koch, Martina Friese, Lorenzo Gentile, and Frederik Rehbach. 2019 · 2019
Later among the works it cites.
mlrMBO: Bayesian Optimization and Model-Based Optimization of Expensive Black-Box Functions – Version 1.1.2
Bernd Bischl, Jakob Richter, Jakob Bossek, Daniel Horn, Michel Lang, and Janek Thomas. 2019 · 2019
Later among the works it cites.
smoof: Single and Multi-Objective Optimization Test Functions – Version 1.5.1
Jakob Bossek and Pascal Kerschke. 2017 · 2019
Later among the works it cites.
COmparing Continuous Optimizers: numbbo/COCO on Github
Nikolaus Hansen, Dimo Brockhoff, Olaf Mersmann, Tea Tusar, Dejan Tusar, Ouassim Ait ElHara, Phillipe R. Sampaio, Asma Atamna, Konstantinos Varelas, Umut Batu, Duc Manh Nguyen, Filip Matzner, and Anne Auger. 2019 · 2019
Later among the works it cites.
Towards self-adaptive efficient global optimization. In Proceedings LeGO – 14th International Global Optimization Workshop
Hao Wang, Michael Emmerich, and Thomas Bäck. 2019 · 2070
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