{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T17:11:29Z","timestamp":1773249089772,"version":"3.50.1"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031147135","type":"print"},{"value":"9783031147142","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-14714-2_3","type":"book-chapter","created":{"date-parts":[[2022,8,13]],"date-time":"2022-08-13T21:03:13Z","timestamp":1660424593000},"page":"32-45","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Non-elitist Selection Can Improve the\u00a0Performance of\u00a0Irace"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8707-4189","authenticated-orcid":false,"given":"Furong","family":"Ye","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3040-7162","authenticated-orcid":false,"given":"Diederick","family":"Vermetten","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4981-3227","authenticated-orcid":false,"given":"Carola","family":"Doerr","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6768-1478","authenticated-orcid":false,"given":"Thomas","family":"B\u00e4ck","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"key":"3_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1007\/978-3-540-73368-3_41","volume-title":"Computer Aided Verification","author":"D Babi\u0107","year":"2007","unstructured":"Babi\u0107, D., Hu, A.J.: Structural abstraction of software verification conditions. In: Damm, W., Hermanns, H. (eds.) CAV 2007. LNCS, vol. 4590, pp. 366\u2013378. Springer, Heidelberg (2007). https:\/\/doi.org\/10.1007\/978-3-540-73368-3_41"},{"key":"3_CR2","unstructured":"Babic, D., Hutter, F.: Spear theorem prover. Solver description, SAT competition 2007 (2007)"},{"key":"3_CR3","doi-asserted-by":"crossref","unstructured":"Bartz-Beielstein, T.: SPOT: an R package for automatic and interactive tuning of optimization algorithms by sequential parameter optimization. CoRR abs\/1006.4645 (2010)","DOI":"10.1007\/978-3-642-02538-9_14"},{"key":"3_CR4","doi-asserted-by":"crossref","unstructured":"Basmer, M., Kehrer, T.: Encoding adaptability of software engineering tools as algorithm configuration problem: a case study. In: Proceedings of International Conference on Automated Software Engineering Workshop (ASEW 2019), pp. 86\u201389. IEEE (2019)","DOI":"10.1109\/ASEW.2019.00035"},{"key":"3_CR5","unstructured":"Bromiley, P., Thacker, N., Bouhova-Thacker, E.: Shannon entropy, Renyi entropy, and information. In: Statistics and Information Series, pp. 1\u20138 (2004)"},{"key":"3_CR6","unstructured":"Cavicchio, D.: Adaptive search using simulated evolution. Ph.D. thesis, University of Michigan (1970)"},{"key":"3_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/978-3-030-72904-2_2","volume-title":"Evolutionary Computation in Combinatorial Optimization","author":"C Cintrano","year":"2021","unstructured":"Cintrano, C., Ferrer, J., L\u00f3pez-Ib\u00e1\u00f1ez, M., Alba, E.: Hybridization of racing methods with evolutionary operators for simulation optimization of traffic lights programs. In: Zarges, C., Verel, S. (eds.) EvoCOP 2021. LNCS, vol. 12692, pp. 17\u201333. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-72904-2_2"},{"issue":"2","key":"3_CR8","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1109\/TEVC.2017.2704781","volume":"22","author":"A Cully","year":"2017","unstructured":"Cully, A., Demiris, Y.: Quality and diversity optimization: a unifying modular framework. IEEE Trans. Evol. Comput. 22(2), 245\u2013259 (2017)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"3_CR9","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1613\/jair.1.11420","volume":"64","author":"K Eggensperger","year":"2019","unstructured":"Eggensperger, K., Lindauer, M., Hutter, F.: Pitfalls and best practices in algorithm configuration. J. Artif. Intell. Res. 64, 861\u2013893 (2019)","journal-title":"J. Artif. Intell. Res."},{"key":"3_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1007\/978-3-319-45823-6_81","volume-title":"Parallel Problem Solving from Nature \u2013 PPSN XIV","author":"W Gao","year":"2016","unstructured":"Gao, W., Nallaperuma, S., Neumann, F.: Feature-based diversity optimization for problem instance classification. In: Handl, J., Hart, E., Lewis, P.R., L\u00f3pez-Ib\u00e1\u00f1ez, M., Ochoa, G., Paechter, B. (eds.) PPSN 2016. LNCS, vol. 9921, pp. 869\u2013879. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-45823-6_81"},{"issue":"2","key":"3_CR11","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1007\/s00453-016-0214-z","volume":"78","author":"C Gie\u00dfen","year":"2017","unstructured":"Gie\u00dfen, C., Witt, C.: The interplay of population size and mutation probability in the $$(1+\\lambda )$$ EA on OneMax. Algorithmica 78(2), 587\u2013609 (2017)","journal-title":"Algorithmica"},{"key":"3_CR12","unstructured":"Goldberg, D.E., Richardson, J., et al.: Genetic algorithms with sharing for multimodal function optimization. In: Proceedings of International Conference on Genetic Algorithms (ICGA 1987), vol. 4149 (1987)"},{"key":"3_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1007\/978-3-642-25566-3_40","volume-title":"Learning and Intelligent Optimization","author":"F Hutter","year":"2011","unstructured":"Hutter, F., Hoos, H.H., Leyton-Brown, K.: Sequential model-based optimization for general algorithm configuration. In: Coello, C.A.C. (ed.) LION 2011. LNCS, vol. 6683, pp. 507\u2013523. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-25566-3_40"},{"key":"3_CR14","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)","journal-title":"J. Artif. Intell. Res."},{"key":"3_CR15","series-title":"The Springer Series on Challenges in Machine Learning","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/978-3-030-05318-5_4","volume-title":"Automated Machine Learning","author":"L Kotthoff","year":"2019","unstructured":"Kotthoff, L., Thornton, C., Hoos, H.H., Hutter, F., Leyton-Brown, K.: Auto-WEKA: automatic model selection and hyperparameter optimization in WEKA. In: Hutter, F., Kotthoff, L., Vanschoren, J. (eds.) Automated Machine Learning. TSSCML, pp. 81\u201395. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-05318-5_4"},{"issue":"2","key":"3_CR16","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1162\/EVCO_a_00025","volume":"19","author":"J Lehman","year":"2011","unstructured":"Lehman, J., Stanley, K.O.: Abandoning objectives: evolution through the search for novelty alone. Evol. Comput. 19(2), 189\u2013223 (2011)","journal-title":"Evol. Comput."},{"issue":"1","key":"3_CR17","first-page":"6765","volume":"18","author":"L Li","year":"2017","unstructured":"Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., Talwalkar, A.: Hyperband: a novel bandit-based approach to hyperparameter optimization. J. Mach. Learn. Res. 18(1), 6765\u20136816 (2017)","journal-title":"J. Mach. Learn. Res."},{"key":"3_CR18","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1007\/978-3-319-63516-3_15","volume-title":"Handbook of Parallel Constraint Reasoning","author":"M Lindauer","year":"2018","unstructured":"Lindauer, M., Hoos, H., Hutter, F., Leyton-Brown, K.: Selection and Configuration of Parallel Portfolios. In: Handbook of Parallel Constraint Reasoning, pp. 583\u2013615. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-63516-3_15"},{"key":"3_CR19","first-page":"43","volume":"3","author":"M L\u00f3pez-Ib\u00e1\u00f1ez","year":"2016","unstructured":"L\u00f3pez-Ib\u00e1\u00f1ez, M., Dubois-Lacoste, J., C\u00e1ceres, L.P., Birattari, M., St\u00fctzle, T.: The Irace package: iterated racing for automatic algorithm configuration. Oper. Res. Perspect. 3, 43\u201358 (2016)","journal-title":"Oper. Res. Perspect."},{"key":"3_CR20","unstructured":"L\u00f3pez-Ib\u00e1\u00f1ez, M., Dubois-Lacoste, J., C\u00e1ceres, L.P., Birattari, M., St\u00fctzle, T.: Irace: iterated racing for automatic algorithm configuration. github.com\/cran\/irace (2020). commit: bae6ae86f2ee0fab9e3270801343482600f095e7"},{"issue":"3","key":"3_CR21","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1016\/j.ejor.2013.10.043","volume":"235","author":"M L\u00f3pez-Ib\u00e1nez","year":"2014","unstructured":"L\u00f3pez-Ib\u00e1nez, M., St\u00fctzle, T.: Automatically improving the anytime behaviour of optimisation algorithms. Eur. J. Oper. Res. 235(3), 569\u2013582 (2014)","journal-title":"Eur. J. Oper. Res."},{"key":"3_CR22","doi-asserted-by":"publisher","unstructured":"L\u00f3pez-Ib\u00e1\u00f1ez, M., St\u00fctzle, T., Dorigo, M.: Ant colony optimization: a component-wise overview. In: Marti, R., Panos, P., Resende, M. (eds.) Handbook of Heuristics, pp. 1\u201337. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-07153-4_21-1","DOI":"10.1007\/978-3-319-07153-4_21-1"},{"key":"3_CR23","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1016\/j.ins.2014.08.002","volume":"289","author":"CS de Magalh\u00e3es","year":"2014","unstructured":"de Magalh\u00e3es, C.S., Almeida, D.M., Barbosa, H.J.C., Dardenne, L.E.: A dynamic niching genetic algorithm strategy for docking highly flexible ligands. Inf. Sci. 289, 206\u2013224 (2014)","journal-title":"Inf. Sci."},{"key":"3_CR24","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.ins.2016.03.039","volume":"355\u2013356","author":"D Mart\u00edn","year":"2016","unstructured":"Mart\u00edn, D., Alcal\u00e1-Fdez, J., Rosete, A., Herrera, F.: NICGAR: a niching genetic algorithm to mine a diverse set of interesting quantitative association rules. Inf. Sci. 355\u2013356, 208\u2013228 (2016)","journal-title":"Inf. Sci."},{"key":"3_CR25","doi-asserted-by":"crossref","unstructured":"Neumann, A., Gao, W., Doerr, C., Neumann, F., Wagner, M.: Discrepancy-based evolutionary diversity optimization. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2018), pp. 991\u2013998. ACM (2018)","DOI":"10.1145\/3205455.3205532"},{"issue":"1","key":"3_CR26","first-page":"3","volume":"5","author":"CE Shannon","year":"2001","unstructured":"Shannon, C.E.: A mathematical theory of communication. ACM SIGMOBILE: Mob. Comput. Commun. Rev. 5(1), 3\u201355 (2001)","journal-title":"ACM SIGMOBILE: Mob. Comput. Commun. Rev."},{"key":"3_CR27","unstructured":"St\u00fctzle, T.: ACOTSP: a software package of various ant colony optimization algorithms applied to the symmetric traveling salesman problem (2002). www.aco-metaheuristic.org\/aco-code"},{"key":"3_CR28","doi-asserted-by":"crossref","unstructured":"Sudholt, D.: Crossover speeds up building-block assembly. In: Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2012), pp. 689\u2013702. ACM (2012)","DOI":"10.1145\/2330163.2330260"},{"key":"3_CR29","doi-asserted-by":"crossref","unstructured":"Wang, H., van Stein, B., Emmerich, M., B\u00e4ck, T.: A new acquisition function for bayesian optimization based on the moment-generating function. In: Proceedings of International Conference on Systems, Man, and Cybernetics (SMC 2017), pp. 507\u2013512. IEEE (2017)","DOI":"10.1109\/SMC.2017.8122656"},{"key":"3_CR30","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2022.3159087","author":"F Ye","year":"2022","unstructured":"Ye, F., Doerr, C., Wang, H., B\u00e4ck, T.: Automated configuration of genetic algorithms by tuning for anytime performance. IEEE Trans. Evol. Comput. (2022). https:\/\/doi.org\/10.1109\/TEVC.2022.3159087","journal-title":"IEEE Trans. Evol. Comput."},{"key":"3_CR31","doi-asserted-by":"publisher","unstructured":"Ye, F., Vermetten, D., Doerr, C., B\u00e4ck, T.: Data Sets for the study \u201cNon-Elitist Selection Can Improve the Performance of Irace\u201d (2022). https:\/\/doi.org\/10.5281\/zenodo.6457959","DOI":"10.5281\/zenodo.6457959"}],"container-title":["Lecture Notes in Computer Science","Parallel Problem Solving from Nature \u2013 PPSN XVII"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-14714-2_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T16:40:33Z","timestamp":1710261633000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-14714-2_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031147135","9783031147142"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-14714-2_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"14 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PPSN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Parallel Problem Solving from Nature","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dortmund","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ppsn2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ppsn2022.cs.tu-dortmund.de\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"185","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"85","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"46% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.75","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.11","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}