Learning Enabled Constrained Black-Box Optimization

[1]  Céline Helbert,et al.  Gaussian process optimization with failures: classification and convergence proof , 2020, Journal of Global Optimization.

[2]  Antonio Candelieri,et al.  Bayesian Optimization and Data Science , 2019, SpringerBriefs in Optimization.

[3]  Manuel Henner,et al.  Databases Coupling for Morphed-Mesh Simulations and Application on Fan Optimal Design , 2019, WCGO.

[4]  Rommel G. Regis,et al.  A Survey of Surrogate Approaches for Expensive Constrained Black-Box Optimization , 2019, WCGO.

[5]  Jiang Yao,et al.  Predicting critical drug concentrations and torsadogenic risk using a multiscale exposure-response simulator. , 2019, Progress in biophysics and molecular biology.

[6]  Warren B. Powell,et al.  A unified framework for stochastic optimization , 2019, Eur. J. Oper. Res..

[7]  Paris Perdikaris,et al.  Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models , 2019, Computer Methods in Applied Mechanics and Engineering.

[8]  Stefan M. Wild,et al.  Derivative-free optimization methods , 2019, Acta Numerica.

[9]  Christian Daniel,et al.  Meta-Learning Acquisition Functions for Bayesian Optimization , 2019, ArXiv.

[10]  Francesco Archetti,et al.  Sequential model based optimization with black-box constraints: Feasibility determination via machine learning , 2019 .

[11]  Yue Cao,et al.  Bayesian active learning for optimization and uncertainty quantification in protein docking , 2019, bioRxiv.

[12]  Guilherme Ottoni,et al.  Constrained Bayesian Optimization with Noisy Experiments , 2017, Bayesian Analysis.

[13]  Marius Lindauer,et al.  Pitfalls and Best Practices in Algorithm Configuration , 2017, J. Artif. Intell. Res..

[14]  Panos M. Pardalos,et al.  No Free Lunch Theorem: A Review , 2019, Approximation and Optimization.

[15]  Michael N. Vrahatis,et al.  Multi-Objective Evolutionary Optimization Algorithms for Machine Learning: A Recent Survey , 2019, Approximation and Optimization.

[16]  Ilya Lebedev,et al.  A flexible generator of constrained global optimization test problems , 2019 .

[17]  Hao Huang,et al.  STOCHASTIC OPTIMIZATION FOR FEASIBILITY DETERMINATION: AN APPLICATION TO WATER PUMP OPERATION IN WATER DISTRIBUTION NETWORK , 2018, 2018 Winter Simulation Conference (WSC).

[18]  Panos M. Pardalos,et al.  A multi-objective evolutionary algorithm based on decomposition and constraint programming for the multi-objective team orienteering problem with time windows , 2018, Appl. Soft Comput..

[19]  Hai Huang,et al.  Stacking sequence optimization and blending design of laminated composite structures , 2018, Structural and Multidisciplinary Optimization.

[20]  Douglas Allaire,et al.  Multi-information source constrained Bayesian optimization , 2018, Structural and Multidisciplinary Optimization.

[21]  Rommel G. Regis,et al.  Accelerated Random Search for constrained global optimization assisted by Radial Basis Function surrogates , 2018, J. Comput. Appl. Math..

[22]  Benjamin Peherstorfer,et al.  Survey of multifidelity methods in uncertainty propagation, inference, and optimization , 2018, SIAM Rev..

[23]  Régis Duvigneau,et al.  A classification approach to efficient global optimization in presence of non-computable domains , 2018 .

[24]  Yaroslav D. Sergeyev,et al.  Guest editors’ preface to the special issue devoted to the 2nd International Conference “Numerical Computations: Theory and Algorithms”, June 19–25, 2016, Pizzo Calabro, Italy , 2018, J. Glob. Optim..

[25]  Zuomin Dong,et al.  SCGOSR: Surrogate-based constrained global optimization using space reduction , 2018, Appl. Soft Comput..

[26]  Francesco Archetti,et al.  Bayesian optimization of pump operations in water distribution systems , 2018, J. Glob. Optim..

[27]  Joseph Morlier,et al.  Efficient global optimization for high-dimensional constrained problems by using the Kriging models combined with the partial least squares method , 2018 .

[28]  Jack P. C. Kleijnen,et al.  Efficient global optimisation for black-box simulation via sequential intrinsic Kriging , 2018, J. Oper. Res. Soc..

[29]  Atharv Bhosekar,et al.  Advances in surrogate based modeling, feasibility analysis, and optimization: A review , 2018, Comput. Chem. Eng..

[30]  Prateek Jain,et al.  Non-convex Optimization for Machine Learning , 2017, Found. Trends Mach. Learn..

[31]  Jack P. C. Kleijnen,et al.  Kriging : Methods and Applications , 2017 .

[32]  Yaroslav D. Sergeyev,et al.  Emmental-Type GKLS-Based Multiextremal Smooth Test Problems with Non-linear Constraints , 2017, LION.

[33]  Nikolaos V. Sahinidis,et al.  The ALAMO approach to machine learning , 2017, Comput. Chem. Eng..

[34]  Kirthevasan Kandasamy,et al.  Multi-fidelity Bayesian Optimisation with Continuous Approximations , 2017, ICML.

[35]  Aaron Klein,et al.  Learning Curve Prediction with Bayesian Neural Networks , 2016, ICLR.

[36]  Taimoor Akhtar,et al.  Efficient Hyperparameter Optimization for Deep Learning Algorithms Using Deterministic RBF Surrogates , 2016, AAAI.

[37]  Julien Bect,et al.  A Bayesian approach to constrained single- and multi-objective optimization , 2015, Journal of Global Optimization.

[38]  Karen Willcox,et al.  Bayesian Optimization with a Finite Budget: An Approximate Dynamic Programming Approach , 2016, NIPS.

[39]  Misha Denil,et al.  Learning to Learn for Global Optimization of Black Box Functions , 2016, ArXiv.

[40]  N. Hansen,et al.  CMA-ES and Advanced Adaptation Mechanisms , 2016, Annual Conference on Genetic and Evolutionary Computation.

[41]  Daniele Venturi,et al.  Multifidelity Information Fusion Algorithms for High-Dimensional Systems and Massive Data sets , 2016, SIAM J. Sci. Comput..

[42]  Nikolaos V. Sahinidis,et al.  Simulation optimization: a review of algorithms and applications , 2014, 4OR.

[43]  Sébastien Le Digabel,et al.  Modeling an Augmented Lagrangian for Blackbox Constrained Optimization , 2014, Technometrics.

[44]  Antanas Zilinskas,et al.  Stochastic Global Optimization: A Review on the Occasion of 25 Years of Informatica , 2016, Informatica.

[45]  Thomas Bäck,et al.  A New Repair Method For Constrained Optimization , 2015, GECCO.

[46]  Alkis Gotovos,et al.  Safe Exploration for Optimization with Gaussian Processes , 2015, ICML.

[47]  Sébastien Le Digabel,et al.  A Taxonomy of Constraints in Simulation-Based Optimization , 2015, 1505.07881.

[48]  Matthew W. Hoffman,et al.  Predictive Entropy Search for Bayesian Optimization with Unknown Constraints , 2015, ICML.

[49]  V. Grishagin,et al.  Multidimensional Constrained Global Optimization in Domains with Computable Boundaries , 2015 .

[50]  Christine A. Shoemaker,et al.  A General Stochastic Algorithmic Framework for Minimizing Expensive Black Box Objective Functions Based on Surrogate Models and Sensitivity Analysis , 2014, 1410.6271.

[51]  Matt J. Kusner,et al.  Bayesian Optimization with Inequality Constraints , 2014, ICML.

[52]  David C. Miller,et al.  Learning surrogate models for simulation‐based optimization , 2014 .

[53]  C. Shoemaker,et al.  Combining radial basis function surrogates and dynamic coordinate search in high-dimensional expensive black-box optimization , 2013 .

[54]  Stephen J. Wright,et al.  Optimization for Machine Learning , 2013 .

[55]  A. Basudhar,et al.  Constrained efficient global optimization with support vector machines , 2012, Structural and Multidisciplinary Optimization.

[56]  S. Kakade,et al.  Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting , 2012, IEEE Transactions on Information Theory.

[57]  N. Trayanova,et al.  A Computational Model to Predict the Effects of Class I Anti-Arrhythmic Drugs on Ventricular Rhythms , 2011, Science Translational Medicine.

[58]  Robert B. Gramacy,et al.  Optimization Under Unknown Constraints , 2010, 1004.4027.

[59]  Andreas Krause,et al.  Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting , 2009, IEEE Transactions on Information Theory.

[60]  Roman Garnett,et al.  Sequential Bayesian prediction in the presence of changepoints , 2009, ICML '09.

[61]  Arnold Neumaier,et al.  SNOBFIT -- Stable Noisy Optimization by Branch and Fit , 2008, TOMS.

[62]  Warren B. Powell,et al.  A Knowledge-Gradient Policy for Sequential Information Collection , 2008, SIAM J. Control. Optim..

[63]  Christine A. Shoemaker,et al.  Parallel radial basis function methods for the global optimization of expensive functions , 2007, Eur. J. Oper. Res..

[64]  Christine A. Shoemaker,et al.  A Stochastic Radial Basis Function Method for the Global Optimization of Expensive Functions , 2007, INFORMS J. Comput..

[65]  G. Box,et al.  Response Surfaces, Mixtures and Ridge Analyses , 2007 .

[66]  Yaroslav D. Sergeyev,et al.  A one-dimensional local tuning algorithm for solving GO problems with partially defined constraints , 2007, Optim. Lett..

[67]  Christine A. Shoemaker,et al.  Constrained Global Optimization of Expensive Black Box Functions Using Radial Basis Functions , 2005, J. Glob. Optim..

[68]  Yaroslav D. Sergeyev,et al.  Algorithm 829: Software for generation of classes of test functions with known local and global minima for global optimization , 2003, TOMS.

[69]  Zelda B. Zabinsky,et al.  Stochastic Adaptive Search for Global Optimization , 2003 .

[70]  Peter Auer,et al.  Using Confidence Bounds for Exploitation-Exploration Trade-offs , 2003, J. Mach. Learn. Res..

[71]  Donald R. Jones,et al.  A Taxonomy of Global Optimization Methods Based on Response Surfaces , 2001, J. Glob. Optim..

[72]  Donald R. Jones,et al.  Efficient Global Optimization of Expensive Black-Box Functions , 1998, J. Glob. Optim..

[73]  L. Rudenko Objective functional approximation in a partially defined optimization problem , 1994 .

[74]  F. Archetti,et al.  A probabilistic algorithm for global optimization , 1979 .

[75]  Harold J. Kushner,et al.  A New Method of Locating the Maximum Point of an Arbitrary Multipeak Curve in the Presence of Noise , 1964 .