{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,30]],"date-time":"2025-03-30T18:03:23Z","timestamp":1743357803680,"version":"3.37.3"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"7-9","license":[{"start":{"date-parts":[[2021,5,4]],"date-time":"2021-05-04T00:00:00Z","timestamp":1620086400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,5,4]],"date-time":"2021-05-04T00:00:00Z","timestamp":1620086400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"None","award":["0"],"award-info":[{"award-number":["0"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Ann Math Artif Intell"],"published-print":{"date-parts":[[2022,9]]},"DOI":"10.1007\/s10472-020-09726-y","type":"journal-article","created":{"date-parts":[[2021,5,4]],"date-time":"2021-05-04T06:03:02Z","timestamp":1620108182000},"page":"715-734","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Boosting evolutionary algorithm configuration"],"prefix":"10.1007","volume":"90","author":[{"given":"Carlos","family":"Ans\u00f3tegui","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Josep","family":"Pon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4513-8180","authenticated-orcid":false,"given":"Meinolf","family":"Sellmann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,5,4]]},"reference":[{"key":"9726_CR1","doi-asserted-by":"crossref","unstructured":"Ahmadizadeh, K., Dilkina, B., Gomes, C.P., Sabharwal, A.: An empirical study of optimization for maximizing diffusion in networks. In: Proceedings of the 16th International Conference on Principles and Practice of Constraint Programming, pp. 514\u2013521 (2010)","DOI":"10.1007\/978-3-642-15396-9_41"},{"key":"9726_CR2","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.artint.2015.12.006","volume":"235","author":"C Ans\u00f3tegui","year":"2016","unstructured":"Ans\u00f3tegui, C., Gab\u00e0s, J., Malitsky, Y., Sellmann, M.: Maxsat by improved instance-specific algorithm configuration. Artif. Intell. 235, 26\u201339 (2016)","journal-title":"Artif. Intell."},{"key":"9726_CR3","unstructured":"Ans\u00f3tegui, C., Malitsky, Y., Samulowitz, H., Sellmann, M., Tierney, K.: Model-based genetic algorithms for algorithm configuration. In: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25-31, 2015, pp. 733\u2013739 (2015)"},{"key":"9726_CR4","doi-asserted-by":"crossref","unstructured":"Ansotegui, C., Sellmann, M., Tierney, K.: A gender-based genetic algorithm for the automatic configuration of algorithms. In: Proceedings of the 15th International Conference on Principles and Practice of Constraint Programming, pp. 142\u2013157 (2009)","DOI":"10.1007\/978-3-642-04244-7_14"},{"key":"9726_CR5","doi-asserted-by":"crossref","unstructured":"Birattari, M., Yuan, Z., Balaprakash, P., St\u00fctzle, T.: F-race and iterated f-race: An overview Empirical Methods for the Analysis of Optimization Algorithms, pp. 311\u2013336 (2010)","DOI":"10.1007\/978-3-642-02538-9_13"},{"key":"9726_CR6","unstructured":"\u015een, A., Atamt\u00fcrk, A., Kaminsky, P.: A conic integer programming approach to constrained assortment optimization under the mixed multinomial logit model. Research Report BCOL.15.06, IEOR University of California\u2013Berkeley (2015)"},{"key":"9726_CR7","unstructured":"Feurer, M., Klein, A., Eggensperger, K., Springenberg, J., Blum, M., Hutter, F.: Efficient and robust automated machine learning. In: Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems. http:\/\/papers.nips.cc\/paper\/5872-efficient-and-robust-automated-machine-learning.pdf, vol. 28, pp 2962\u20132970. Curran Associates, Inc. (2015)"},{"key":"9726_CR8","unstructured":"Hamerly, G., Elkan, C.: Learning the k in k-means. In: In Neural Information Processing Systems, p. 2003. MIT Press (2003)"},{"issue":"4\u20135","key":"9726_CR9","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1017\/S1471068414000210","volume":"14","author":"HH Hoos","year":"2014","unstructured":"Hoos, H.H., Lindauer, M., Schaub, T.: claspfolio 2: Advances in algorithm selection for answer set programming. Theory Pract. Logic Program. 14(4\u20135), 569\u2013585 (2014). https:\/\/doi.org\/10.1017\/S1471068414000210https:\/\/doi.org\/10.1017\/S1471068414000210","journal-title":"Theory Pract. Logic Program."},{"key":"9726_CR10","unstructured":"Hutter, F., Hamadi, Y.: Parameter adjustment based on performance prediction: Towards an instance-aware problem solver. Tech. Rep. MSR-TR-2005-125, Microsoft Research, Cambridge UK (2005)"},{"key":"9726_CR11","doi-asserted-by":"crossref","unstructured":"Hutter, F., Hoos, H., Leyton-Brown, K.: Sequential model-based optimization for general algorithm configuration. In: Proceedings of the 5th International Conference on Learning and Intelligent Optimization, pp. 507\u2013523 (2011)","DOI":"10.1007\/978-3-642-25566-3_40"},{"key":"9726_CR12","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1613\/jair.2861","volume":"36","author":"F Hutter","year":"2009","unstructured":"Hutter, F., Hoos, H.H., Leyton-Brown, K., St\u00fctzle, T.: Paramils: An automatic algorithm configuration framework. J. Artif. Intell. Res. 36, 267\u2013306 (2009). https:\/\/doi.org\/10.1613\/jair.2861","journal-title":"J. Artif. Intell. Res."},{"key":"9726_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.artint.2016.09.006","volume":"243","author":"F Hutter","year":"2017","unstructured":"Hutter, F., Lindauer, M., Balint, A., Bayless, S., Hoos, H., Leyton-Brown, K.: The configurable sat solver challenge (cssc). Artif. Intell. 243, 1\u201325 (2017). https:\/\/doi.org\/10.1016\/j.artint.2016.09.006","journal-title":"Artif. Intell."},{"key":"9726_CR14","doi-asserted-by":"crossref","unstructured":"Hutter, F., L\u00f3pez-Ib\u00e1\u00f1ez, M., Fawcett, C., Lindauer, M., Hoos, H.H., Leyton-Brown, K., St\u00fctzle, T.: Aclib: A benchmark library for algorithm configuration. In: Pardalos, P. M., Resende, M. G., Vogiatzis, C., Walteros, J. L. (eds.) Learning and Intelligent Optimization, pp 36\u201340. Springer International Publishing, Cham (2014)","DOI":"10.1007\/978-3-319-09584-4_4"},{"key":"9726_CR15","doi-asserted-by":"crossref","unstructured":"Kadioglu, S., Malitsky, Y., Sabharwal, A., Samulowitz, H., Sellmann, M.: Algorithm selection and scheduling. CP, 454\u2013469 (2011)","DOI":"10.1007\/978-3-642-23786-7_35"},{"key":"9726_CR16","unstructured":"Kadioglu, S., Malitsky, Y., Sellmann, M., Tierney, K.: ISAC\u2013Instance-Specific Algorithm Configuration. In: Coelho, H., Studer, R., Wooldridge, M. (eds.) Proceedings of the 19th European Conference on Artificial Intelligence (ECAI), Frontiers in Artificial Intelligence and Applications, vol. 215, pp. 751\u2013756. IOS Press (2010)"},{"key":"9726_CR17","unstructured":"Leyton-Brown, K., Nudelman, E., Andrew, G., McFadden, J., Shoham, Y.: A portfolio approach to algorithm selection. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence, pp. 1542\u20131543 (2003)"},{"key":"9726_CR18","unstructured":"Lindauer, M., Feurer, M., Eggensperger, K., Klein, A., Falkner, S., Hutter, F.: Parallel SMAC (pSMAC) (2018). https:\/\/automl.github.io\/SMAC3\/stable\/psmac.html"},{"key":"9726_CR19","unstructured":"Lindauer, M., Hutter, F.: Warmstarting of model-based algorithm configuration. In: McIlraith, S.A., Weinberger, K.Q. (eds.) Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018, pp. 1355\u20131362. AAAI Press. https:\/\/www.aaai.org\/ocs\/index.php\/AAAI\/AAAI18\/paper\/view\/17235 (2018)"},{"key":"9726_CR20","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.orp.2016.09.002","volume":"3","author":"M L\u00f3pez-Ib\u00e1\u00f1ez","year":"2016","unstructured":"L\u00f3pez-Ib\u00e1\u00f1ez, M., Dubois-Lacoste, J., P\u00e9rez C\u00e1ceres, L., St\u00fctzle, T., Birattari, M.: The irace package: Iterated racing for automatic algorithm configuration. Oper. Res. Perspect. 3, 43\u201358 (2016). https:\/\/doi.org\/10.1016\/j.orp.2016.09.002, http:\/\/iridia.ulb.ac.be\/supp\/IridiaSupp2016-003\/","journal-title":"Oper. Res. Perspect."},{"key":"9726_CR21","unstructured":"Malitsky, Y., Sabharwal, A., Samulowitz, H., Sellmann, M.: Algorithm portfolios based on cost-sensitive hierarchical clustering. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence, pp. 608\u2013614 (2013)"},{"key":"9726_CR22","unstructured":"MaxSAT-Evaluations. https:\/\/maxsat-evaluations.github.io\/ (2019)"},{"issue":"2","key":"9726_CR23","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1016\/j.ejor.2013.12.013","volume":"235","author":"DJ Papageorgiou","year":"2014","unstructured":"Papageorgiou, D.J., Nemhauser, G.L., Sokol, J., Cheon, M.S., Keha, A.B.: Mirplib \u2013 a library of maritime inventory routing problem instances: Survey, core model, and benchmark results. EJOR 235(2), 350\u2013366 (2014). https:\/\/doi.org\/10.1016\/j.ejor.2013.12.013. Maritime Logistics","journal-title":"EJOR"},{"key":"9726_CR24","unstructured":"SAT-Competition: (2019). www.satcompetition.org"},{"key":"9726_CR25","unstructured":"Sheldon, D., Dilkina, B., Elmachtoub, A., Finseth, R., Sabharwal, A., Conrad, J., Gomes, C.P., Shmoys, D., Allen, W., Amundsen, O., Vaughan, B.: Maximizing spread of cascades using network design. In: UAI-2010: 26th Conference on Uncertainty in Artificial Intelligence, pp 517\u2013526, Catalina Island (2010)"},{"key":"9726_CR26","doi-asserted-by":"crossref","unstructured":"Xu, L., Hoos, H.H., Leyton-Brown, K.: Hydra: Automatically configuring algorithms for portfolio-based selection. AAAI (2010)","DOI":"10.1609\/aaai.v24i1.7565"},{"key":"9726_CR27","unstructured":"Xu, L., Hutter, F., Hoos, H.H., Leyton-Brown, K.: Satzilla2009: An automatic algorithm portfolio for sat. solver description (2009). SAT Competition"},{"key":"9726_CR28","unstructured":"Xu, L., Hutter, F., Shen, J., Hoos, H.H., Leyton-Brown, K.: Satzilla2012: Improved algorithm selection based on cost-sensitive classification models (2012). SAT Competition"},{"issue":"11","key":"9726_CR29","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1145\/2934664","volume":"59","author":"M Zaharia","year":"2016","unstructured":"Zaharia, M., Xin, R.S., Wendell, P., Das, T., Armbrust, M., Dave, A., Meng, X., Rosen, J., Venkataraman, S., Franklin, M.J., Ghodsi, A., Gonzalez, J., Shenker, S., Stoica, I.: Apache spark: A unified engine for big data processing. Commun. ACM 59(11), 56\u201365 (2016). https:\/\/doi.org\/10.1145\/2934664","journal-title":"Commun. ACM"}],"container-title":["Annals of Mathematics and Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10472-020-09726-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10472-020-09726-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10472-020-09726-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,26]],"date-time":"2022-12-26T08:41:57Z","timestamp":1672044117000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10472-020-09726-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,4]]},"references-count":29,"journal-issue":{"issue":"7-9","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["9726"],"URL":"https:\/\/doi.org\/10.1007\/s10472-020-09726-y","relation":{},"ISSN":["1012-2443","1573-7470"],"issn-type":[{"type":"print","value":"1012-2443"},{"type":"electronic","value":"1573-7470"}],"subject":[],"published":{"date-parts":[[2021,5,4]]},"assertion":[{"value":"27 December 2020","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 May 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}