Efficient Global Optimization of Expensive Black-Box Functions
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Non-linear models encompassing non-parametric, kernel-based and tree-ensemble approaches enable a comprehensive comparison.
“These non-linear models encompass non-parametric (KNN), kernel-based (KRR, SVR and GP) and tree-ensemble (RFR, XGB and ETR) approaches, enabling a comprehensive comparison34,35,47.”
From Data-driven de novo design of super-adhesive hydrogels (2025) · cites this paper as [47]
Cites
Numerical recipes in Pascal: the art of scientific computing
(1990) · Choice Reviews Online · doi:10.5860/choice.27-5158 · Cited by 12,040 · see citing works
Cressie, N. (1992) · Terra Nova · doi:10.1111/j.1365-3121.1992.tb00605.x · Cited by 9,139 · see citing works
Vetterling, W. T., Teukolsky, S. A., Press, W. H., & Flannery, B. P. (1988) · Cited by 7,701 · see citing works
McKay, M. D., Beckman, R. J., & Conover, W. J. (2000) · Technometrics · doi:10.2307/1271432 · Cited by 7,551 · see citing works
Addelman, S., Box, G. E. P., Hunter, W. G., & Hunter, J. S. (1979) · Technometrics · doi:10.2307/1267766 · Cited by 6,753 · see citing works
Cressie, N. A. C. (1993) · Wiley Series in Probability and Statistics · doi:10.1002/9781119115151 · Cited by 6,507 · see citing works
5. Statistics for Spatial Data
Rees, M., & Cressie, N. (1993) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.2307/2982871 · Cited by 4,983 · see citing works
Matheron, G. (1963) · Economic Geology · doi:10.2113/gsecongeo.58.8.1246 · Cited by 4,934 · see citing works
Hannan, E. J., & Theil, H. (1973) · Technometrics · doi:10.2307/1266838 · Cited by 3,190 · see citing works
Cressie, N. (1990) · Mathematical Geology · doi:10.1007/bf00889887 · Cited by 1,829 · see citing works
A New Method of Locating the Maximum Point of an Arbitrary Multipeak Curve in the Presence of Noise
Kushner, H. J. (1964) · Journal of Basic Engineering · doi:10.1115/1.3653121 · Cited by 1,103 · see citing works
Currin, C., Mitchell, T. J., Morris, M. D., & Ylvisaker, D. (1991) · Journal of the American Statistical Association · doi:10.1080/01621459.1991.10475138 · Cited by 744 · see citing works
A trust-region framework for managing the use of approximation models in optimization
Alexandrov, N. M., Dennis, J. E., Lewis, R. M., & Torczon, V. J. (1998) · Structural and Multidisciplinary Optimization · doi:10.1007/bf01197433 · Cited by 642 · see citing works
Screening, Predicting, and Computer Experiments
Welch, W. J., Buck, R. J., Sacks, J., Wynn, H. P., Mitchell, T. J., & Morris, M. D. (1992) · Technometrics · doi:10.2307/1269548 · Cited by 633 · see citing works
Self-citation?BB: A global optimization method for general constrained nonconvex problems
Androulakis, I. P., Maranas, C. D., & Floudas, C. A. (1995) · Journal of Global Optimization · doi:10.1007/bf01099647 · Cited by 468 · see citing works
Global versus local search in constrained optimization of computer models
Schonlau, M., Welch, W. J., & Jones, D. R. (1998) · Lecture notes-monograph series · doi:10.1214/lnms/1215456182 · Cited by 444 · see citing works
Self-citationApplication of Bayesian approach to numerical methods of global and stochastic optimization
Mockus, J. (1994) · Journal of Global Optimization · doi:10.1007/bf01099263 · Cited by 400 · see citing works
Computer experiments and global optimization
Welch, W. J., & Schonlau, M. (1997) · Cited by 377 · see citing works
Self-citationA statistical method for global optimization
Cox, D. D., & John, S. (2003) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.1992.271617 · Cited by 309 · see citing works
Bayesian Design and Analysis of Computer Experiments: Use of Derivatives in Surface Prediction
Morris, M. D., Mitchell, T. J., & Ylvisaker, D. (1993) · Technometrics · doi:10.1080/00401706.1993.10485320 · Cited by 300 · see citing works
Bayesian design and analysis of computer experiments: Use of derivatives in surface prediction
Morris, M. D., Mitchell, T. J., & Ylvisaker, D. (1991) · doi:10.2172/5836957 · Cited by 276 · see citing works
Multivariable spatial prediction
Hoef, J. M. V., & Cressie, N. (1993) · Mathematical Geology · doi:10.1007/bf00893273 · Cited by 172 · see citing works
Bayesian Algorithms for One-Dimensional Global Optimization
Locatelli, M. (1997) · Journal of Global Optimization · doi:10.1023/a:1008294716304 · Cited by 122 · see citing works
Cressie, N. (1989) · The American Statistician · doi:10.1080/00031305.1989.10475658 · Cited by 99 · see citing works
Bayesian methods in global optimization
Betr�, B. (1991) · Journal of Global Optimization · doi:10.1007/bf00120661 · Cited by 97 · see citing works
A global search method for optimizing nonlinear systems
Stuckman, B. E. (1988) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/21.23094 · Cited by 77 · see citing works
A review of statistical models for global optimization
Žilinskas, A. (1992) · Journal of Global Optimization · doi:10.1007/bf00122051 · Cited by 74 · see citing works
Boender, C. G. E., & Romeijn, H. E. (1995) · Nonconvex optimization and its applications · doi:10.1007/978-1-4615-2025-2_15 · Cited by 68 · see citing works
Evaluation of Injection Island GA Performance on Flywheel Design Optimisation
Eby, D. J., Averill, R. C., Punch, W. F., & Goodman, E. D. (1998) · Springer eBooks · doi:10.1007/978-1-4471-1589-2_10 · Cited by 58 · see citing works
Circuit Optimization via Sequential Computer Experiments: Design of an Output Buffer
Aslett, R., Buck, R. J., Duvall, S. G., Sacks, J., & Welch, W. J. (1998) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/1467-9876.00096 · Cited by 45 · see citing works
Self-citation5. A New Approach to the Synthesis of Optimal Smoothing and Prediction Systems
Parzen, E. (1963) · University of California Press eBooks · doi:10.1525/9780520319875-007 · Cited by 33 · see citing works
Predicting Urban Ozone Levels and Trends with Semiparametric Modeling
Gao, F., Sacks, J., & Welch, W. J. (1996) · Journal of Agricultural Biological and Environmental Statistics · doi:10.2307/1400436 · Cited by 21 · see citing works
Self-citationGlobal R/sup d/ optimization when probes are expensive: the GROPE algorithm
Elder, J. F. (2003) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.1992.271711 · Cited by 18 · see citing works
A computational geometric approach to feasible region division in constrained global optimization
Perttunen, C. D. (2002) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.1991.169748 · Cited by 9 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Taking the Human Out of the Loop: A Review of Bayesian Optimization
Shahriari, B., Swersky, K., Wang, Z., Adams, R. P., & de Freitas, N. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2494218 · Cited by 6,195 · see citing works
Recent advances and applications of machine learning in solid-state materials science
Schmidt, J., Marques, M. R. G., Botti, S., & Marques, M. A. L. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0221-0 · Cited by 2,655 · see citing works
Sequential Model-Based Optimization for General Algorithm Configuration
Hutter, F., Hoos, H. H., & Leyton-Brown, K. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-25566-3_40 · Cited by 2,469 · see citing works
Surrogate-based analysis and optimization
Queipo, N. V., Haftka, R. T., Shyy, W., Goel, T., Vaidyanathan, R., & Tucker, P. K. (2005) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2005.02.001 · Cited by 2,465 · see citing works
Machine learning and the physical sciences
Carleo, G., Cirac, J. I., Cranmer, K. S., Daudet, L., Schuld, M., Tishby, N., Vogt-Maranto, L., & Zdeborová, L. (2019) · Reviews of Modern Physics · doi:10.1103/revmodphys.91.045002 · Cited by 2,453 · see citing works
Recent advances in surrogate-based optimization
Forrester, A. I. J., & Keane, A. J. (2009) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2008.11.001 · Cited by 2,382 · see citing works
Engineering Design via Surrogate Modelling: A Practical Guide
Forrester, A. I. J., Sóbester, A., & Keane, A. J. (2008) · doi:10.1002/9780470770801 · Cited by 2,309 · see citing works
Hyperparameters and tuning strategies for random forest
Probst, P., Wright, M. N., & Boulesteix, A. (2019) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1301 · Cited by 2,319 · see citing works
A Taxonomy of Global Optimization Methods Based on Response Surfaces
Jones, D. R. (2001) · Journal of Global Optimization · doi:10.1023/a:1012771025575 · Cited by 2,160 · see citing works
Self-citationInformation-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting
Srinivas, N., Krause, A., Kakade, S. M., & Seeger, M. (2012) · IEEE Transactions on Information Theory · doi:10.48550/arxiv.0912.3995 · Cited by 2,100 · see citing works
Probabilistic machine learning and artificial intelligence
Ghahramani, Z. (2015) · Nature · doi:10.1038/nature14541 · Cited by 1,995 · see citing works
Brochu, E., Cora, V. M., & de Freitas, N. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1012.2599 · Cited by 2,568 · see citing works
AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation
Echard, B., Gayton, N., & Lemaire, M. (2011) · Structural Safety · doi:10.1016/j.strusafe.2011.01.002 · Cited by 2,050 · see citing works
Review of Metamodeling Techniques in Support of Engineering Design Optimization
Wang, G. G., & Shan, S. (2006) · Journal of Mechanical Design · doi:10.1115/1.2429697 · Cited by 1,718 · see citing works
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules.
Gómez‐Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera‐Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru‐Guzik, A. (2018) · Cambridge University Engineering Department Publications Database · doi:10.17863/cam.34134 · Cited by 1,813 · see citing works
A Tutorial on Bayesian OptimizationPreprint
Frazier, P. I. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.02811 · Cited by 1,423 · see citing works
Derivative-free optimization: a review of algorithms and comparison of software implementations
Rios, L. M., & Sahinidis, N. V. (2013) · Journal of Global Optimization · doi:10.1007/s10898-012-9951-y · Cited by 1,276 · see citing works
Kriging metamodeling in simulation: A review
Kleijnen, J. P. C. (2007) · European Journal of Operational Research · doi:10.1016/j.ejor.2007.10.013 · Cited by 1,279 · see citing works
Feurer, M., & Hutter, F. (2019) · The Springer Series on Challenges in Machine Learning · doi:10.1007/978-3-030-05318-5_1 · Cited by 1,414 · see citing works
Knowles, J. D. (2006) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2005.851274 · Cited by 1,261 · see citing works
Efficient Global Reliability Analysis for Nonlinear Implicit Performance Functions
Bichon, B. J., Eldred, M., Swiler, L. P., Mahadevan, S., & McFarland, J. M. (2008) · AIAA Journal · doi:10.2514/1.34321 · Cited by 1,141 · see citing works
Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization
Peherstorfer, B., Willcox, K. E., & Gunzburger, M. (2018) · SIAM Review · doi:10.1137/16m1082469 · Cited by 1,080 · see citing works
Bayesian reaction optimization as a tool for chemical synthesis
Shields, B. J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J. I. M., Janey, J. M., Adams, R. P., & Doyle, A. G. (2021) · Nature · doi:10.1038/s41586-021-03213-y · Cited by 1,063 · see citing works
Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms
Thornton, C., Hutter, F., Hoos, H. H., & Leyton-Brown, K. (2013) · Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2487575.2487629 · Cited by 1,359 · see citing works
Review of surrogate modeling in water resources
Razavi, S., Tolson, B. A., & Burn, D. H. (2012) · Water Resources Research · doi:10.1029/2011wr011527 · Cited by 994 · see citing works
Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges
Bischl, B., Binder, M., Lang, M., Pielok, T., Richter, J., Coors, S., Thomas, J., Ullmann, T., Becker, M., Boulesteix, A., et al. (2023) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1484 · Cited by 1,118 · see citing works
Machine learning assisted design of high entropy alloys with desired property
Wen, C., Zhang, Y., Wang, C., Xue, D., Bai, Y., Antonov, S., Dai, L.-H., Lookman, T., & Su, Y. (2019) · Acta Materialia · doi:10.1016/j.actamat.2019.03.010 · Cited by 946 · see citing works
Science Requirements and Detector Concepts for the Electron-Ion Collider
Khalek, R. A., Accardi, A., Adam, J., Adamiak, D., Akers, W., Albaladejo, M., Al-bataineh, A., Alexeev, M., Ameli, F., Antonioli, P., et al. (2022) · Nuclear Physics A · doi:10.1016/j.nuclphysa.2022.122447 · Cited by 1,133 · see citing works
A Radial Basis Function Method for Global Optimization
Gutmann, H.-M. (2001) · Journal of Global Optimization · doi:10.1023/a:1011255519438 · Cited by 910 · see citing works
Frazier, P. I. (2018) · doi:10.1287/educ.2018.0188 · Cited by 887 · see citing works
Lookman, T., Balachandran, P. V., Xue, D., & Yuan, R. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0153-8 · Cited by 873 · see citing works
Use of Kriging Models to Approximate Deterministic Computer Models
Martin, J. D., & Simpson, T. W. (2005) · AIAA Journal · doi:10.2514/1.8650 · Cited by 840 · see citing works
Information-seeking, curiosity, and attention: computational and neural mechanisms
Gottlieb, J., Oudeyer, P.-Y., Lopes, M., & Baranes, A. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.09.001 · Cited by 784 · see citing works
Accelerated search for materials with targeted properties by adaptive design
Xue, D., Balachandran, P. V., Hogden, J., Theiler, J. P., Xue, D., & Lookman, T. (2016) · Nature Communications · doi:10.1038/ncomms11241 · Cited by 801 · see citing works
Eldred, M., Dalbey, K., Bohnhoff, W., Adams, B., Swiler, L. P., Hough, P., Gay, D., Eddy, J., & Haskell, K. (2010) · doi:10.2172/991842 · Cited by 775 · see citing works
Choosing the Sample Size of a Computer Experiment: A Practical Guide
Loeppky, J. L., Sacks, J., & Welch, W. J. (2009) · Technometrics · doi:10.1198/tech.2009.08040 · Cited by 768 · see citing works
Self-citationSingle- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels
Emmerich, M., Giannakoglou, K. C., & Naujoks, B. (2006) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2005.859463 · Cited by 763 · see citing works
Expensive Multiobjective Optimization by MOEA/D With Gaussian Process Model
Zhang, Q., Liu, W., Tsang, E. P. K., & Virginas, B. (2009) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2009.2033671 · Cited by 748 · see citing works
Advances in surrogate based modeling, feasibility analysis, and optimization: A review
Bhosekar, A., & Ierapetritou, M. (2017) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2017.09.017 · Cited by 720 · see citing works
Echard, B., Gayton, N., Lemaire, M., & Relun, N. (2012) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2012.10.008 · Cited by 692 · see citing works
Data-Driven Evolutionary Optimization: An Overview and Case Studies
Jin, Y., Wang, H., Chugh, T., Guo, D., & Miettinen, K. (2018) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2018.2869001 · Cited by 696 · see citing works
Auto-WEKA: Automatic Model Selection and Hyperparameter Optimization in WEKA
Kotthoff, L., Thornton, C., Hoos, H. H., Hutter, F., & Leyton‐Brown, K. (2019) · The Springer series on challenges in machine learning · doi:10.1007/978-3-030-05318-5_4 · Cited by 671 · see citing works
Lago, J., De Ridder, F., & De Schutter, B. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.02.069 · Cited by 642 · see citing works
Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models
Huang, D.-Y., Allen, T. T., Notz, W. I., & Zeng, N. (2006) · Journal of Global Optimization · doi:10.1007/s10898-005-2454-3 · Cited by 625 · see citing works
Schratz, P., Muenchow, J., Iturritxa, E., Richter, J., & Brenning, A. (2018) · Ecological Modelling · doi:10.1016/j.ecolmodel.2019.06.002 · Cited by 482 · see citing works
Shan, S., & Wang, G. G. (2009) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-009-0420-2 · Cited by 598 · see citing works
A review of surrogate models and their application to groundwater modeling
Asher, M. J., Croke, B., Jakeman, A. J., & Peeters, L. J. M. (2015) · Water Resources Research · doi:10.1002/2015wr016967 · Cited by 597 · see citing works
Roustant, O., Ginsbourger, D., & Deville, Y. (2012) · Journal of Statistical Software · doi:10.18637/jss.v051.i01 · Cited by 595 · see citing works
Tunability: Importance of Hyperparameters of Machine Learning AlgorithmsPreprint
Probst, P., Bischl, B., & Boulesteix, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.09596 · Cited by 590 · see citing works
Liu, B., Zhang, Q., & Gielen, G. E. (2013) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2013.2248012 · Cited by 581 · see citing works
Simulation optimization: a review of algorithms and applications
Amaran, S., Sahinidis, N. V., Sharda, B., & Bury, S. J. (2015) · Annals of Operations Research · doi:10.1007/s10479-015-2019-x · Cited by 545 · see citing works
Dealing with uncertainty in model updating for damage assessment: a review
Simoen, E., De Roeck, G., & Lombaert, G. (2015) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2014.11.001 · Cited by 540 · see citing works
Efficient Optimization Design Method Using Kriging Model
Jeong, S., Murayama, M., & Yamamoto, K. (2005) · Journal of Aircraft · doi:10.2514/1.6386 · Cited by 536 · see citing works
A Stochastic Radial Basis Function Method for the Global Optimization of Expensive Functions
Regis, R. G., & Shoemaker, C. A. (2007) · INFORMS journal on computing · doi:10.1287/ijoc.1060.0182 · Cited by 530 · see citing works
Bayesian Optimization for Adaptive Experimental Design: A Review
Greenhill, S., Rana, S., Gupta, S., Vellanki, P., & Venkatesh, S. (2020) · IEEE Access · doi:10.1109/access.2020.2966228 · Cited by 526 · see citing works
Hierarchical Kriging Model for Variable-Fidelity Surrogate Modeling
Han, Z., & Görtz, S. (2012) · AIAA Journal · doi:10.2514/1.j051354 · Cited by 492 · see citing works
Recent Advances in Bayesian Optimization
Wang, X., Jin, Y., Schmitt, S., & Olhofer, M. (2023) · ACM Computing Surveys · doi:10.1145/3582078 · Cited by 491 · see citing works
Generalizing Surrogate-Assisted Evolutionary Computation
Lim, D., Jin, Y., Ong, Y.-S., & Sendhoff, B. (2009) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2009.2027359 · Cited by 487 · see citing works
Hyper-Parameter Optimization: A Review of Algorithms and ApplicationsPreprint
Yu, T., & Zhu, H. C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.05689 · Cited by 729 · see citing works
Adaptive Submodularity: A New Approach to Active Learning and Stochastic OptimizationPreprint
Golovin, D., & Krause, A. (2010) · Annual Conference Computational Learning Theory · doi:10.48550/arxiv.1003.3967 · Cited by 139 · see citing works
A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design
Gorissen, D., Couckuyt, I., Demeester, P. M. A., Dhaene, T., & Crombecq, K. (2010) · Ghent University Academic Bibliography (Ghent University) · Cited by 475 · see citing works
Pan, L., He, C., Tian, Y., Wang, H., Zhang, X., & Jin, Y. (2018) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2018.2802784 · Cited by 461 · see citing works
A review of recent advances in global optimization
Floudas, C. A., & Gounaris, C. E. (2009) · Journal of Global Optimization · doi:10.1007/s10898-008-9332-8 · Cited by 455 · see citing works
Learning surrogate models for simulation‐based optimization
Cozad, A., Sahinidis, N. V., & Miller, D. C. (2014) · AIChE Journal · doi:10.1002/aic.14418 · Cited by 459 · see citing works
Active learning of inverse models with intrinsically motivated goal exploration in robots
Baranès, A., & Oudeyer, P. (2012) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2012.05.008 · Cited by 466 · see citing works
Entropy Search for Information-Efficient Global OptimizationPreprint
Hennig, P., & Schuler, C. J. (2011) · arXiv (Cornell University) · doi:10.5555/2188385.2343701 · Cited by 474 · see citing works
Managing computational complexity using surrogate models: a critical review
Alizadeh, R., Allen, J. K., & Mistree, F. (2020) · Research in Engineering Design · doi:10.1007/s00163-020-00336-7 · Cited by 460 · see citing works
Derivative-free optimization methods
Larson, J. M., Menickelly, M., & Wild, S. M. (2019) · Acta Numerica · doi:10.1017/s0962492919000060 · Cited by 440 · see citing works
Convergence rates of efficient global optimization algorithmsPreprint
Bull, A. D. (2011) · arXiv (Cornell University) · doi:10.5555/1953048.2078198 · Cited by 454 · see citing works
Reliability-based design optimization using kriging surrogates and subset simulation
Dubourg, V., Sudret, B., & Bourinet, J. (2011) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-011-0653-8 · Cited by 450 · see citing works
Liu, H., Ong, Y.-S., & Cai, J. (2017) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-017-1739-8 · Cited by 446 · see citing works
LIF: A new Kriging based learning function and its application to structural reliability analysis
Sun, Z., Wang, J., Li, R., & Tong, C. (2016) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2016.09.003 · Cited by 444 · see citing works
Combining Global and Local Surrogate Models to Accelerate Evolutionary Optimization
Zhou, Z., Ong, Y.-S., Nair, P. B., Keane, A. J., & Lum, K. (2006) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2005.855506 · Cited by 432 · see citing works
Complete search in continuous global optimization and constraint satisfaction
Neumaier, A. (2004) · Acta Numerica · doi:10.1017/s0962492904000194 · Cited by 432 · see citing works
Exploration of Metamodeling Sampling Criteria for Constrained Global Optimization
Sasena, M. J., Papalambros, P. Y., & Goovaerts, P. (2002) · Engineering Optimization · doi:10.1080/03052150211751 · Cited by 428 · see citing works
Domhan, T., Springenberg, J. T., & Hutter, F. (2015) · FreiDok plus (Universitätsbibliothek Freiburg) · Cited by 431 · see citing works
Kriging Is Well-Suited to Parallelize Optimization
Ginsbourger, D., Riche, R. L., & Carraro, L. (2010) · Adaptation, learning, and optimization · doi:10.1007/978-3-642-10701-6_6 · Cited by 420 · see citing works
Wang, H., Jin, Y., & Doherty, J. (2017) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2710978 · Cited by 417 · see citing works
Sequential kriging optimization using multiple-fidelity evaluations
Huang, D.-Y., Allen, T. T., Notz, W. I., & Miller, R. (2006) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-005-0587-0 · Cited by 415 · see citing works
Huang, X., Chen, J., & Zhu, H. (2016) · Structural Safety · doi:10.1016/j.strusafe.2015.12.003 · Cited by 412 · see citing works
The Knowledge-Gradient Policy for Correlated Normal Beliefs
Frazier, P. I., Powell, W. B., & Dayanik, S. (2009) · INFORMS journal on computing · doi:10.1287/ijoc.1080.0314 · Cited by 407 · see citing works
Initializing Bayesian Hyperparameter Optimization via Meta-Learning
Feurer, M., Springenberg, J. T., & Hutter, F. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9354 · Cited by 402 · see citing works
POLYNOMIAL-CHAOS-BASED KRIGING
Schöbi, R., Sudret, B., & Wiart, J. (2015) · International Journal for Uncertainty Quantification · doi:10.1615/int.j.uncertaintyquantification.2015012467 · Cited by 399 · see citing works
Accelerated Discovery of Large Electrostrains in BaTiO3‐Based Piezoelectrics Using Active Learning
Yuan, R., Liu, Z., Balachandran, P. V., Xue, D., Zhou, Y., Ding, X., Sun, J., Xue, D., & Lookman, T. (2018) · Advanced Materials · doi:10.1002/adma.201702884 · Cited by 395 · see citing works
Algorithm runtime prediction: Methods & evaluation
Hutter, F., Xu, L., Hoos, H. H., & Leyton‐Brown, K. (2013) · Artificial Intelligence · doi:10.1016/j.artint.2013.10.003 · Cited by 424 · see citing works
Bayesian Optimization in a Billion Dimensions via Random Embeddings
Wang, Z., Hutter, F., Zoghi, M., Matheson, D., & Feitas, N. D. (2016) · Journal of Artificial Intelligence Research · doi:10.1613/jair.4806 · Cited by 401 · see citing works
Currin, A., Swainston, N., Day, P. J., & Kell, D. B. (2014) · Chemical Society Reviews · doi:10.1039/c4cs00351a · Cited by 384 · see citing works
Multi-fidelity deep neural network surrogate model for aerodynamic shape optimization
Zhang, X., Xie, F., Ji, T., Zhu, Z., & Zheng, Y. (2020) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2020.113485 · Cited by 382 · see citing works
Phoenics: A Bayesian Optimizer for Chemistry
Häse, F., Roch, L. M., Kreisbeck, C., & Aspuru-Guzik, A. (2018) · ACS Central Science · doi:10.1021/acscentsci.8b00307 · Cited by 379 · see citing works
Kůdela, J., & Matoušek, R. (2022) · Soft Computing · doi:10.1007/s00500-022-07362-8 · Cited by 379 · see citing works
Application of Generative Autoencoder in <i>De Novo</i> Molecular Design
Blaschke, T., Olivecrona, M., Engkvist, O., Bajorath, J., & Chen, H. (2018) · Molecular Informatics · doi:10.1002/minf.201700123 · Cited by 411 · see citing works
Cherrypick: adaptively unearthing the best cloud configurations for big data analytics
Alipourfard, O., Liu, H. H., Chen, J., Venkataraman, S., Yu, M., & Zhang, M. (2017) · Networked Systems Design and Implementation · Cited by 378 · see citing works
Viana, F. A. C., Simpson, T. W., Balabanov, V. O., & TOROPOV, V. V. (2014) · AIAA Journal · doi:10.2514/1.j052375 · Cited by 377 · see citing works
A Survey of Automatic Parameter Tuning Methods for Metaheuristics
Huang, C., Li, Y., & Yao, X. (2019) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2019.2921598 · Cited by 373 · see citing works
Hyperparameter Search in Machine LearningPreprint
Claesen, M., & Moor, B. D. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1502.02127 · Cited by 496 · see citing works
Machine learning-based methods in structural reliability analysis: A review
Afshari, S. S., Enayatollahi, F., Xu, X., & Liang, X. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.108223 · Cited by 370 · see citing works
Grammar Variational AutoencoderPreprint
Kusner, M. J., Paige, B., & Hernández-Lobato, J. M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.01925 · Cited by 367 · see citing works
Machine learning in aerodynamic shape optimization
Li, J., Du, X., & Martins, J. R. R. A. (2022) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2022.100849 · Cited by 376 · see citing works
Autonomous Discovery in the Chemical Sciences Part I: Progress
Coley, C. W., Eyke, N. S., & Jensen, K. F. (2019) · Angewandte Chemie International Edition · doi:10.1002/anie.201909987 · Cited by 365 · see citing works
Vanschoren, J. (2019) · The Springer series on challenges in machine learning · doi:10.1007/978-3-030-05318-5_2 · Cited by 364 · see citing works
- Authors
- Jones, D. R., Schonlau, M., & Welch, W. J.
- Affiliations
- General Motors (United States) (US)
- National Institute of Statistical Sciences (US)
- University of Waterloo (CA)
- Venue
- Journal of Global Optimization
- Published
- 1998-12
- Volume
- 13
- Issue
- 4
- Pages
- 455-492
- ISSN
- 0925-5001 (ISSN-L)
- 1573-2916
- OpenAlex
- W1510052597
- Cited by
- 8,070
- References
- 34
- Citations by year
- 2026: 730 · 2025: 661 · 2024: 601 · 2023: 599 · 2022: 636 · 2021: 659 · 2020: 612 · 2019: 548 · 2018: 458 · 2017: 369 · 2016: 370 · 2015: 284 · 2014: 255 · 2013: 219 · 2012: 215
- FWCI
- 7.84
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Global Optimization (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Numerical Analysis › Advanced Optimization Algorithms Research
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Advanced Multi-Objective Optimization Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Keywords
- expensive black-box functions, global optimization, response surface methodology, surrogate models, stopping rules, exploration-exploitation balance, prediction error, engineering design optimization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
