{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T09:01:23Z","timestamp":1784019683695,"version":"3.55.0"},"reference-count":125,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["FL170100006"],"award-info":[{"award-number":["FL170100006"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.2966228","type":"journal-article","created":{"date-parts":[[2020,1,13]],"date-time":"2020-01-13T20:40:13Z","timestamp":1578948013000},"page":"13937-13948","source":"Crossref","is-referenced-by-count":479,"title":["Bayesian Optimization for Adaptive Experimental Design: A Review"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7585-9632","authenticated-orcid":false,"given":"Stewart","family":"Greenhill","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2247-850X","authenticated-orcid":false,"given":"Santu","family":"Rana","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4669-9940","authenticated-orcid":false,"given":"Sunil","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8910-8533","authenticated-orcid":false,"given":"Pratibha","family":"Vellanki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8675-6631","authenticated-orcid":false,"given":"Svetha","family":"Venkatesh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2487629"},{"key":"ref38","first-page":"2546","article-title":"Algorithms for hyper-parameter optimization","author":"bergstra","year":"2011","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref33","first-page":"1015","article-title":"Gaussian process optimization in the bandit setting: No regret and experimental design","author":"srinivas","year":"2010","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref32","article-title":"The application of Bayesian methods for seeking the extremum","author":"mockus","year":"1978","journal-title":"Toward Global Optimization 2"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1115\/1.3653121"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-018-03657-3"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/4235.873238"},{"key":"ref36","first-page":"170","article-title":"A view of algorithms for optimization without derivatives","volume":"43","author":"powell","year":"2007","journal-title":"Math Today-Bull Inst Math Appl"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/BF01589116"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/BF00941892"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.physb.2017.03.006"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ECC.2016.7810598"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/2858036.2858253"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2015.2494218"},{"key":"ref22","article-title":"A tutorial on Bayesian optimization","author":"frazier","year":"2018","journal-title":"arXiv 1807 02811"},{"key":"ref21","article-title":"A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning","author":"brochu","year":"2010","journal-title":"ArXiv 1012 2599"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/s11837-018-2984-z"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms11241"},{"key":"ref101","first-page":"3126","article-title":"The parallel knowledge gradient method for batch Bayesian optimization","author":"wu","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevX.7.021024"},{"key":"ref100","first-page":"3330","article-title":"Parallel predictive entropy search for batch global optimization of expensive objective functions","author":"shah","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-017-05723-0"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1016\/j.cossms.2016.10.002"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1038\/nmat4717"},{"key":"ref59","first-page":"307","article-title":"Regret bounds for transfer learning in Bayesian optimisation","author":"shilton","year":"2017","journal-title":"Proc Artif Intell Statist"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-31753-3_9"},{"key":"ref57","first-page":"1077","article-title":"Efficient transfer learning method for automatic hyperparameter tuning","author":"yogatama","year":"2014","journal-title":"Proc 14th Int Conf Artif Intell Statist (AISTATS)"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-10-6781-5"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.115.205901"},{"key":"ref54","article-title":"Discovery of low thermal conductivity compounds with first-principles anharmonic lattice dynamics calculations and Bayesian optimization","author":"seko","year":"2015","journal-title":"arXiv 1506 06439"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.89.054303"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-23871-5_3"},{"key":"ref40","first-page":"2960","article-title":"Practical Bayesian optimization of machine learning algorithms","author":"snoek","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1115\/1.2429697"},{"key":"ref3","article-title":"Response surface methodology: Process and product optimization using designed experiments","author":"myers","year":"2009","journal-title":"Applied Probability and Statistics"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1080\/09544820802275557"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1080\/00401706.1989.10488520"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-12385-1_33"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1016\/j.md.2016.04.001"},{"key":"ref7","first-page":"123","article-title":"A framework for the application of robust design methods and tools","author":"g\u00f6hler","year":"2014","journal-title":"1st International symposium on robust design"},{"key":"ref9","doi-asserted-by":"crossref","DOI":"10.1007\/0-387-28014-6","author":"dean","year":"2006","journal-title":"Screening Methods for Experimentation in Industry Drug Discovery and Genetics"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/s10472-015-9463-9"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1038\/nature14422"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2016.01.032"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2018.00028"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.114.111801"},{"key":"ref41","first-page":"2004","article-title":"Multi-task Bayesian optimization","author":"swersky","year":"2013","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-25566-3_40"},{"key":"ref43","first-page":"1778","article-title":"Bayesian optimization in high dimensions via random embeddings","author":"wang","year":"2013","journal-title":"Proc Int Joint Conf Artif Intell"},{"key":"ref125","article-title":"GPflow: A Gaussian process library using TensorFlow","author":"knudde","year":"2017","journal-title":"arXiv 1711 03845"},{"key":"ref124","article-title":"Parallel Bayesian global optimization of expensive functions","author":"wang","year":"2016","journal-title":"arXiv 1602 05149"},{"key":"ref73","first-page":"2883","article-title":"High dimensional Bayesian optimization with elastic Gaussian process","author":"rana","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref72","first-page":"268","author":"dai nguyen","year":"2016","journal-title":"Cascade Bayesian Optimization"},{"key":"ref71","article-title":"A kernel for hierarchical parameter spaces","author":"hutter","year":"2013","journal-title":"arXiv 1310 5738"},{"key":"ref70","first-page":"295","article-title":"High dimensional Bayesian optimisation and bandits via additive models","author":"kandasamy","year":"2015","journal-title":"Proc 32nd Int Conf Mach Learn (ICML)"},{"key":"ref76","first-page":"884","article-title":"High dimensional Bayesian optimization via restricted projection pursuit models","author":"li","year":"2016","journal-title":"Proc Artif Intell Statist"},{"key":"ref77","article-title":"Batched high-dimensional Bayesian optimization via structural kernel learning","author":"wang","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/291"},{"key":"ref75","first-page":"3868","article-title":"BOCK: Bayesian optimization with cylindrical kernels","author":"oh","year":"2018","journal-title":"Proc 35th Int Conf Mach Learn"},{"key":"ref78","first-page":"1311","article-title":"Discovering and exploiting additive structure for Bayesian optimization","author":"gardner","year":"2017","journal-title":"Proc Artif Intell Statist"},{"key":"ref79","first-page":"4752","article-title":"A framework for Bayesian optimization in embedded subspaces","author":"nayebi","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref60","first-page":"199","article-title":"Collaborative hyperparameter tuning","author":"bardenet","year":"2013","journal-title":"Proc 30th Int Conf Mach Learn (ICML)"},{"key":"ref62","article-title":"Bayesian optimization with shape constraints","author":"jauch","year":"2016","journal-title":"arXiv 1612 08915"},{"key":"ref61","first-page":"645","article-title":"Gaussian processes with monotonicity information","author":"riihim\u00e4ki","year":"2010","journal-title":"Proc 13th Int Conf Artif Intell Statist"},{"key":"ref63","article-title":"Bayesian optimization with monotonicity information","author":"li","year":"2017","journal-title":"Proc 31st Conf Neural Inf Process Syst (NIPS)"},{"key":"ref64","first-page":"43","article-title":"Bayesian analysis of shape-restricted functions using Gaussian process priors","volume":"27","author":"lenk","year":"2017","journal-title":"Statistica Sinica"},{"key":"ref65","article-title":"Bayesian optimization of unimodal functions","author":"andersen","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst (NIPS)"},{"key":"ref66","article-title":"Information-theoretic transfer learning framework for Bayesian optimisation","author":"ramachandran","year":"2018","journal-title":"Proc Eur Conf Mach Learn Knowl Discovery Databases"},{"key":"ref67","first-page":"1655","article-title":"Bayesian optimization with tree-structured dependencies","author":"jenatton","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref68","article-title":"Raiders of the lost architecture: Kernels for Bayesian optimization in conditional parameter spaces","author":"swersky","year":"2014","journal-title":"arXiv 1409 4011"},{"key":"ref2","author":"montgomery","year":"2017","journal-title":"Design and Analysis of Experiments"},{"key":"ref69","first-page":"226","article-title":"Additive Gaussian processes","author":"duvenaud","year":"2011","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref1","author":"fisher","year":"1935","journal-title":"The Design of Experiments"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-014-9477-x"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0144"},{"key":"ref108","article-title":"The computation of the expected improvement in dominated hypervolume of Pareto front approximations","author":"emmerich","year":"2008"},{"key":"ref94","first-page":"648","article-title":"Batch Bayesian optimization via local penalization","author":"gonz\u00e1lez","year":"2015","journal-title":"Proc Artif Intell Statist"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-87700-4_78"},{"key":"ref93","first-page":"3873","article-title":"Parallelizing exploration-exploitation tradeoffs in Gaussian process bandit optimization","volume":"15","author":"desautels","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2005.851274"},{"key":"ref92","first-page":"109","article-title":"Batch Bayesian optimization via simulation matching","author":"azimi","year":"2010","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref105","author":"collette","year":"2013","journal-title":"Multiobjective Optimization Principles and Case Studies"},{"key":"ref91","article-title":"A multi-points criterion for deterministic parallel global optimization based on Gaussian processes","author":"ginsbourger","year":"2008"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-21936-3"},{"key":"ref90","article-title":"Multi-objective Bayesian optimisation with preferences over objectives","author":"abdolshah","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst (NIPS)"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1007\/s00158-009-0420-2"},{"key":"ref102","first-page":"3417","article-title":"Process-constrained batch Bayesian optimisation","author":"vellanki","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1007\/s10898-016-0427-3"},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.1561\/2200000016"},{"key":"ref110","first-page":"1492","article-title":"Predictive entropy search for multi-objective Bayesian optimization","author":"hern\u00e1ndez-lobato","year":"2016","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref98","first-page":"538","article-title":"Exploiting strategy-space diversity for batch Bayesian optimization","author":"gupta","year":"2018","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.06.026"},{"key":"ref96","first-page":"2347","article-title":"Quantile stein variational gradient descent for batch Bayesian optimization","author":"gong","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40988-2_15"},{"key":"ref10","first-page":"1","article-title":"Things you wanted to know about the Latin hypercube design and were afraid to ask","author":"viana","year":"2013","journal-title":"Proc 6th World Congr of Structural and Multidisciplinary Optim"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1057\/jos.2013.14"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1951.tb00067.x"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1080\/08982112.2013.852681"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1080\/00224065.2004.11980252"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1214\/ss\/1177012413"},{"key":"ref118","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2014.6907423"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/s00158-017-1739-8"},{"key":"ref82","first-page":"1025","article-title":"High-dimensional Gaussian process bandits","author":"djolonga","year":"2013","journal-title":"Proc Adv Neural Inf Process Syst Annu Conf Neural Inf Process Syst"},{"key":"ref117","first-page":"4291","article-title":"Multi-information source optimization","author":"poloczek","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.2514\/1.J052375"},{"key":"ref81","first-page":"3429","article-title":"Adaptive and safe Bayesian optimization in high dimensions via one-dimensional subspaces","author":"kirschner","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.compchemeng.2017.05.010"},{"key":"ref84","first-page":"937","article-title":"Bayesian optimization with inequality constraints","author":"gardner","year":"2014","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref119","first-page":"2007","article-title":"Selecting near-optimal learners via incremental data allocation","author":"sabharwal","year":"2016","journal-title":"Proc AAAI"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008306431147"},{"key":"ref83","first-page":"250","article-title":"Bayesian optimization with unknown constraints","author":"gelbart","year":"2014","journal-title":"Proc Uncertainty Artif Intell"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2017.7989186"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1098\/rsif.2015.1107"},{"key":"ref116","first-page":"98","article-title":"Towards efficient Bayesian optimization for big data","volume":"134","author":"klein","year":"2015","journal-title":"Proc NIPS Workshop Bayesian Optim"},{"key":"ref80","first-page":"9005","article-title":"Efficient high dimensional Bayesian optimization with additivity and quadrature Fourier features","author":"mutny","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref115","article-title":"Multi-fidelity Bayesian optimisation with continuous approximations","author":"kandasamy","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref120","first-page":"703","article-title":"Active learning from weak and strong labelers","author":"zhang","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref89","first-page":"1435","article-title":"Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian","author":"picheny","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref121","article-title":"Dealing with Categorical and Integer-valued Variables in Bayesian optimization with Gaussian processes","author":"garrido-merch\u00e1n","year":"2017","journal-title":"arXiv 1706 03673"},{"key":"ref122","article-title":"An investigation into new kernels for categorical variables","author":"villegas garc\u00eda","year":"2013"},{"key":"ref123","first-page":"3735","article-title":"BayesOpt: A Bayesian optimization library for nonlinear optimization, experimental design and bandits","volume":"15","author":"martinez-cantin","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref85","first-page":"1","article-title":"ADMMBO: Bayesian optimization with unknown constraints using ADMM","volume":"20","author":"ariafar","year":"2019","journal-title":"J Mach Learn Res"},{"key":"ref86","first-page":"1699","article-title":"Predictive entropy search for Bayesian optimization with unknown constraints","author":"hern\u00e1ndez-lobato","year":"2015","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.06.025"},{"key":"ref88","first-page":"1888","article-title":"Lookahead Bayesian optimization with inequality constraints","author":"lam","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/08957442.pdf?arnumber=8957442","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T15:57:10Z","timestamp":1642003030000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8957442\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":125,"URL":"https:\/\/doi.org\/10.1109\/access.2020.2966228","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}