{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T06:00:15Z","timestamp":1761976815763,"version":"build-2065373602"},"publisher-location":"Berlin, Heidelberg","reference-count":37,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"type":"print","value":"9783642449727"},{"type":"electronic","value":"9783642449734"}],"license":[{"start":{"date-parts":[[2013,1,1]],"date-time":"2013-01-01T00:00:00Z","timestamp":1356998400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2013,1,1]],"date-time":"2013-01-01T00:00:00Z","timestamp":1356998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013]]},"DOI":"10.1007\/978-3-642-44973-4_13","type":"book-chapter","created":{"date-parts":[[2013,11,25]],"date-time":"2013-11-25T08:47:05Z","timestamp":1385369225000},"page":"110-124","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["PROGRESS: Progressive Reinforcement-Learning-Based Surrogate Selection"],"prefix":"10.1007","author":[{"given":"Stefan","family":"Hess","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tobias","family":"Wagner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bernd","family":"Bischl","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2013,11,26]]},"reference":[{"key":"13_CR1","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1109\/CEC.2005.1554761","volume-title":"Proceedings of the 2005 Congress on Evolutionary Computation (CEC\u201905), Edinburgh, Scotland","author":"T Bartz-Beielstein","year":"2005","unstructured":"Bartz-Beielstein, T., Lasarczyk, C.G., Preuss, M.: Sequential parameter optimization. In: McKay, B., et al. (eds.) Proceedings of the 2005 Congress on Evolutionary Computation (CEC\u201905), Edinburgh, Scotland, pp. 773\u2013780. IEEE Press, Los Alamitos (2005)"},{"key":"13_CR2","unstructured":"Bischl, B., Lang, M., Mersmann, O., Rahnenfuehrer, J., Weihs, C.: BatchJobs and BatchExperiments: abstraction mechanisms for using R in batch environments. Submitted to Journal of Statistical Software (2012a)"},{"issue":"2","key":"13_CR3","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1162\/EVCO_a_00069","volume":"20","author":"B Bischl","year":"2012","unstructured":"Bischl, B., Mersmann, O., Trautmann, H., Weihs, C.: Resampling methods for meta-model validation with recommendations for evolutionary computation. Evol. Comput. 20(2), 249\u2013275 (2012b)","journal-title":"Evol. Comput."},{"issue":"3","key":"13_CR4","doi-asserted-by":"publisher","first-page":"1103","DOI":"10.1016\/j.jspi.2004.08.007","volume":"136","author":"D Bursztyn","year":"2006","unstructured":"Bursztyn, D., Steinberg, D.M.: Comparison of designs for computer experiments. J. Stat. Planning Infer. 136(3), 1103\u20131119 (2006)","journal-title":"J. Stat. Planning Infer."},{"key":"13_CR5","first-page":"913","volume-title":"In: Proceedings of the 10th Conference Genetic and Evolutionary Computation (GECCO \u201908)","author":"L DaCosta","year":"2008","unstructured":"DaCosta, L., Fialho, A., Schoenauer, M., Sebag, M.: Adaptive operator selection with dynamic multi-armed bandits. In: Proceedings of the 10th Conference Genetic and Evolutionary Computation (GECCO \u201908), pp. 913\u2013920. ACM, New York (2008)"},{"issue":"1","key":"13_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1214\/aos\/1176347963","volume":"19","author":"J Friedman","year":"1991","unstructured":"Friedman, J.: Multivariate adaptive regression splines. Ann. Stat. 19(1), 1\u201367 (1991)","journal-title":"Ann. Stat."},{"issue":"5","key":"13_CR7","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"J Friedman","year":"2001","unstructured":"Friedman, J.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189\u20131232 (2001)","journal-title":"Ann. Stat."},{"key":"13_CR8","doi-asserted-by":"crossref","unstructured":"Friese, M., Zaefferer, M., Bartz-Beielstein, T., Flasch, O., Koch, P., Konen, W., Naujoks, B.: Ensemble based optimization and tuning algorithms. In: Hoffmann, F., H\u00fcllermeier, E. (eds.) Proceedings of the 21. Workshop Computational Intelligence, pp. 119\u2013134 (2011)","DOI":"10.1145\/2001858.2001926"},{"issue":"6","key":"13_CR9","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1002\/qre.945","volume":"24","author":"D Ginsbourger","year":"2008","unstructured":"Ginsbourger, D., Helbert, C., Carraro, L.: Discrete mixtures of kernels for kriging-based optimization. Qual. Reliab. Eng. Int. 24(6), 681\u2013691 (2008)","journal-title":"Qual. Reliab. Eng. Int."},{"issue":"3","key":"13_CR10","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1007\/s00158-006-0051-9","volume":"33","author":"T Goel","year":"2007","unstructured":"Goel, T., Haftka, R.T., Shyy, W., Queipo, N.V.: Ensemble of surrogates. Struct. Multidisc. Optim. 33(3), 199\u2013216 (2007)","journal-title":"Struct. Multidisc. Optim."},{"key":"13_CR11","first-page":"2039","volume":"10","author":"D Gorissen","year":"2009","unstructured":"Gorissen, D., Dhaene, T., Turck, F.: Evolutionary model type selection for global surrogate modeling. J. Mach. Learn. Res. 10, 2039\u20132078 (2009)","journal-title":"J. Mach. Learn. Res."},{"issue":"10","key":"13_CR12","doi-asserted-by":"publisher","first-page":"993","DOI":"10.1109\/34.58871","volume":"12","author":"L Hansen","year":"1990","unstructured":"Hansen, L., Salamon, P.: Neural network ensembles. IEEE Trans. Pattern Anal. Mach. Intell. 12(10), 993\u20131001 (1990)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"13_CR13","unstructured":"Hansen, N., Finck, S., Ros, R., Auger, A.: Real-Parameter Black-Box Optimization Benchmarking 2009: Noiseless Functions Definitions. Tech. Rep. RR-6829, INRIA (2009). http:\/\/hal.inria.fr\/inria-00362633\/en\/"},{"key":"13_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics","author":"T Hastie","year":"2009","unstructured":"Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics. Springer, New York (2009)"},{"issue":"4","key":"13_CR15","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1023\/A:1012771025575","volume":"21","author":"DR Jones","year":"2001","unstructured":"Jones, D.R.: A taxonomy of global optimization methods based on response surfaces. J. Global Optim. 21(4), 345\u2013383 (2001)","journal-title":"J. Global Optim."},{"issue":"4","key":"13_CR16","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1023\/A:1008306431147","volume":"13","author":"D Jones","year":"1998","unstructured":"Jones, D., Schonlau, M., Welch, W.: Efficient global optimization of expensive black-box functions. J. Global Optim. 13(4), 455\u2013492 (1998)","journal-title":"J. Global Optim."},{"issue":"430","key":"13_CR17","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1080\/01621459.1995.10476572","volume":"90","author":"RE Kass","year":"1995","unstructured":"Kass, R.E., Raftery, A.E.: Bayes factors. J. Am. Stat. Assoc. 90(430), 773\u2013795 (1995)","journal-title":"J. Am. Stat. Assoc."},{"issue":"7","key":"13_CR18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v032.i07","volume":"32","author":"RV Lenth","year":"2009","unstructured":"Lenth, R.V.: Response-surface methods in R, using rsm. J. Stat. Softw. 32(7), 1\u201317 (2009)","journal-title":"J. Stat. Softw."},{"issue":"3","key":"13_CR19","first-page":"18","volume":"2","author":"A Liaw","year":"2002","unstructured":"Liaw, A., Wiener, M.: Classification and regression by randomForest. R News 2(3), 18\u201322 (2002)","journal-title":"R News"},{"key":"13_CR20","doi-asserted-by":"publisher","first-page":"1288","DOI":"10.1145\/1276958.1277203","volume-title":"Proceedings of the 9th Annual Genetic and Evolutionary Computation Conference (GECCO 2007)","author":"D Lim","year":"2007","unstructured":"Lim, D., Ong, Y.S., Jin, Y., Sendhoff, B.: A study on metamodeling techniques, ensembles, and multi-surrogates in evolutionary computation. In: Thierens, D., et al. (eds.) Proceedings of the 9th Annual Genetic and Evolutionary Computation Conference (GECCO 2007), pp. 1288\u20131295. ACM, New York (2007)"},{"key":"13_CR21","unstructured":"Mersmann, O., Bischl, B.: soobench: Single Objective Optimization Benchmark Functions (2012). http:\/\/CRAN.R-project.org\/package=soobench, R package version 1.0-73"},{"key":"13_CR22","unstructured":"Milborrow, S.: earth: Multivariate Adaptive Regression Spline Models (2012). http:\/\/CRAN.R-project.org\/package=earth, R package version 3.2-3"},{"key":"13_CR23","first-page":"117","volume-title":"Towards Global Optimization 2","author":"JB Mockus","year":"1978","unstructured":"Mockus, J.B., Tiesis, V., Zilinskas, A.: The application of bayesian methods for seeking the extremum. In: Dixon, L.C.W., Szeg\u00f6, G.P. (eds.) Towards Global Optimization 2, pp. 117\u2013129. Elsevier North-Holland, New York (1978)"},{"key":"13_CR24","volume-title":"Response Surface Methodology","author":"RH Myers","year":"2009","unstructured":"Myers, R.H., Montgomery, D.C., Anderson-Cook, C.M.: Response Surface Methodology, 3rd edn. Wiley, Hoboken (2009)","edition":"3"},{"issue":"3","key":"13_CR25","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1007\/s00158-013-0919-4","volume":"48","author":"V Picheny","year":"2013","unstructured":"Picheny, V., Wagner, T., Ginsbourger, D.: A benchmark of kriging-based infill criteria for noisy optimization. Struct. Multidisc. Optim. 48(3), 607\u2013626 (2013)","journal-title":"Struct. Multidisc. Optim."},{"key":"13_CR26","unstructured":"R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna (2012). http:\/\/www.R-project.org\/ ISBN 3-900051-07-0"},{"key":"13_CR27","unstructured":"Ridgeway, G.: gbm: Generalized Boosted Regression Models (2012). http:\/\/CRAN.R-project.org\/package=gbm, R package version 1.6-3.2"},{"issue":"1","key":"13_CR28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v051.i01","volume":"51","author":"O Roustant","year":"2012","unstructured":"Roustant, O., Ginsbourger, D., Deville, Y.: DiceKriging, DiceOptim: two R packages for the analysis of computer experiments by kriging-based metamodeling and optimization. J. Stat. Softw. 51(1), 1\u201355 (2012). http:\/\/www.jstatsoft.org\/v51\/i01\/","journal-title":"J. Stat. Softw."},{"key":"13_CR29","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-3799-8","volume-title":"The Sesign and Analysis of Computer Experiments","author":"T Santner","year":"2003","unstructured":"Santner, T., Williams, B., Notz, W.: The Sesign and Analysis of Computer Experiments. Springer, New York (2003)"},{"issue":"3","key":"13_CR30","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1080\/03052150211751","volume":"34","author":"MJ Sasena","year":"2002","unstructured":"Sasena, M.J., Papalambros, P., Goovaerts, P.: Exploration of metamodeling sampling criteria for constrained global optimization. Eng. Optim. 34(3), 263\u2013278 (2002)","journal-title":"Eng. Optim."},{"issue":"2","key":"13_CR31","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1007\/s00158-009-0420-2","volume":"41","author":"S Shan","year":"2010","unstructured":"Shan, S., Wang, G.G.: Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions. Struct. Multi. Optim. 41(2), 219\u2013241 (2010)","journal-title":"Struct. Multi. Optim."},{"key":"13_CR32","volume-title":"Reinforcement Learning: An Introduction","author":"R Sutton","year":"1998","unstructured":"Sutton, R., Barto, A.: Reinforcement Learning: An Introduction. Cambridge University Press, Cambridge (1998)"},{"key":"13_CR33","unstructured":"Therneau, T.M., port by Brian Ripley, B.A.R.: rpart: Recursive Partitioning (2012). http:\/\/CRAN.R-project.org\/package=rpart, R package version 3.1-54"},{"key":"13_CR34","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-21706-2","volume-title":"Modern Applied Statistics with S","author":"WN Venables","year":"2002","unstructured":"Venables, W.N., Ripley, B.D.: Modern Applied Statistics with S, 4th edn. Springer, New York (2002)","edition":"4"},{"key":"13_CR35","unstructured":"Viana, F.A.C.: Multiple Surrogates for Prediction and Optimization. Ph.D. thesis, University of Florida (2011)"},{"key":"13_CR36","first-page":"718","volume-title":"PPSN XI. LNCS","author":"T Wagner","year":"2010","unstructured":"Wagner, T., Emmerich, M., Deutz, A., Ponweiser, W.: On expected-improvement criteria for model-based multi-objective optimization. In: Schaefer, R., Cotta, C., Ko\u0142odziej, J., Rudolph, G. (eds.) PPSN XI. LNCS, vol. 6238, pp. 718\u2013727. Springer, Heidelberg (2010)"},{"key":"13_CR37","doi-asserted-by":"crossref","unstructured":"Wichard, J.D.: Model selection in an ensemble framework. In: International Joint Conference on Neural Networks, pp. 2187\u20132192 (2006)","DOI":"10.1109\/IJCNN.2006.247012"}],"container-title":["Lecture Notes in Computer Science","Learning and Intelligent Optimization"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-642-44973-4_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T22:59:27Z","timestamp":1746053967000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-642-44973-4_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013]]},"ISBN":["9783642449727","9783642449734"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-642-44973-4_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2013]]},"assertion":[{"value":"26 November 2013","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}