{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T13:37:18Z","timestamp":1742996238014,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031147203"},{"type":"electronic","value":"9783031147210"}],"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\/978-3-031-14721-0_36","type":"book-chapter","created":{"date-parts":[[2022,8,15]],"date-time":"2022-08-15T00:02:52Z","timestamp":1660521772000},"page":"512-525","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Running Time Analysis of\u00a0the\u00a0(1+1)-EA Using Surrogate Models on\u00a0OneMax and\u00a0LeadingOnes"],"prefix":"10.1007","author":[{"given":"Zi-An","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Bian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Qian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,8,15]]},"reference":[{"key":"36_CR1","doi-asserted-by":"publisher","DOI":"10.1142\/7438","volume-title":"Theory of Randomized Search Heuristics: Foundations and Recent Developments","author":"A Auger","year":"2011","unstructured":"Auger, A., Doerr, B.: Theory of Randomized Search Heuristics: Foundations and Recent Developments. World Scientific, Singapore (2011)"},{"key":"36_CR2","doi-asserted-by":"publisher","DOI":"10.1093\/oso\/9780195099713.001.0001","volume-title":"Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms","author":"T Back","year":"1996","unstructured":"Back, T.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press, Oxford (1996)"},{"key":"36_CR3","doi-asserted-by":"crossref","unstructured":"Bian, C., Qian, C., Tang, K.: A general approach to running time analysis of multi-objective evolutionary algorithms. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI 2018), Stockholm, Sweden, pp. 1405\u20131411 (2018)","DOI":"10.24963\/ijcai.2018\/195"},{"issue":"5","key":"36_CR4","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1109\/TEVC.2017.2753538","volume":"22","author":"D Corus","year":"2017","unstructured":"Corus, D., Dang, D.C., Eremeev, A.V., Lehre, P.K.: Level-based analysis of genetic algorithms and other search processes. IEEE Trans. Evol. Comput. 22(5), 707\u2013719 (2017)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"1","key":"36_CR5","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1007\/s00453-011-9585-3","volume":"65","author":"B Doerr","year":"2013","unstructured":"Doerr, B., Goldberg, L.A.: Adaptive drift analysis. Algorithmica 65(1), 224\u2013250 (2013)","journal-title":"Algorithmica"},{"issue":"4","key":"36_CR6","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1007\/s00453-012-9622-x","volume":"64","author":"B Doerr","year":"2012","unstructured":"Doerr, B., Johannsen, D., Winzen, C.: Multiplicative drift analysis. Algorithmica 64(4), 673\u2013697 (2012)","journal-title":"Algorithmica"},{"key":"36_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-29414-4","volume-title":"Theory of Evolutionary Computation: Recent Developments in Discrete Optimization","author":"B Doerr","year":"2020","unstructured":"Doerr, B., Neumann, F.: Theory of Evolutionary Computation: Recent Developments in Discrete Optimization. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-29414-4"},{"issue":"1\u20132","key":"36_CR8","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/S0304-3975(01)00182-7","volume":"276","author":"S Droste","year":"2002","unstructured":"Droste, S., Jansen, T., Wegener, I.: On the analysis of the (1+1) evolutionary algorithm. Theor. Comput. Sci. 276(1\u20132), 51\u201381 (2002)","journal-title":"Theor. Comput. Sci."},{"issue":"6","key":"36_CR9","doi-asserted-by":"publisher","first-page":"1125","DOI":"10.1109\/TEVC.2020.2986348","volume":"24","author":"H Hao","year":"2020","unstructured":"Hao, H., Zhang, J., Lu, X., Zhou, A.: Binary relation learning and classifying for preselection in evolutionary algorithms. IEEE Trans. Evol. Comput. 24(6), 1125\u20131139 (2020)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"36_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1007\/978-3-319-95933-7_80","volume-title":"Intelligent Computing Theories and Application","author":"H Hao","year":"2018","unstructured":"Hao, H., Zhang, J., Zhou, A.: A comparison study of surrogate model based preselection in evolutionary optimization. In: Huang, D.-S., Jo, K.-H., Zhang, X.-L. (eds.) ICIC 2018. LNCS, vol. 10955, pp. 717\u2013728. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-95933-7_80"},{"issue":"1","key":"36_CR11","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/S0004-3702(01)00058-3","volume":"127","author":"J He","year":"2001","unstructured":"He, J., Yao, X.: Drift analysis and average time complexity of evolutionary algorithms. Artif. Intell. 127(1), 57\u201385 (2001)","journal-title":"Artif. Intell."},{"issue":"1","key":"36_CR12","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s00500-003-0328-5","volume":"9","author":"Y Jin","year":"2005","unstructured":"Jin, Y.: A comprehensive survey of fitness approximation in evolutionary computation. Soft. Comput. 9(1), 3\u201312 (2005)","journal-title":"Soft. Comput."},{"issue":"2","key":"36_CR13","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.swevo.2011.05.001","volume":"1","author":"Y Jin","year":"2011","unstructured":"Jin, Y.: Surrogate-assisted evolutionary computation: recent advances and future challenges. Swarm Evol. Comput. 1(2), 61\u201370 (2011)","journal-title":"Swarm Evol. Comput."},{"issue":"5","key":"36_CR14","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1109\/TEVC.2002.800884","volume":"6","author":"Y Jin","year":"2002","unstructured":"Jin, Y., Olhofer, M., Sendhoff, B.: A framework for evolutionary optimization with approximate fitness functions. IEEE Trans. Evol. Comput. 6(5), 481\u2013494 (2002)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"36_CR15","series-title":"Studies in Computational Intelligence","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-74640-7","volume-title":"Data-Driven Evolutionary Optimization","author":"Y Jin","year":"2021","unstructured":"Jin, Y., Wang, H., Sun, C.: Data-Driven Evolutionary Optimization. SCI, vol. 975. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-74640-7"},{"issue":"4","key":"36_CR16","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1007\/BF01099263","volume":"4","author":"J Mockus","year":"1994","unstructured":"Mockus, J.: Application of Bayesian approach to numerical methods of global and stochastic optimization. J. Global Optim. 4(4), 347\u2013365 (1994)","journal-title":"J. Global Optim."},{"key":"36_CR17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-16544-3","volume-title":"Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity","author":"F Neumann","year":"2010","unstructured":"Neumann, F., Witt, C.: Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-16544-3"},{"issue":"3","key":"36_CR18","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1007\/s00453-010-9387-z","volume":"59","author":"PS Oliveto","year":"2011","unstructured":"Oliveto, P.S., Witt, C.: Simplified drift analysis for proving lower bounds in evolutionary computation. Algorithmica 59(3), 369\u2013386 (2011)","journal-title":"Algorithmica"},{"key":"36_CR19","doi-asserted-by":"crossref","unstructured":"Qian, C., Xiong, H., Xue, K.: Bayesian optimization using pseudo-points. In: Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI 2020), Yokohama, Japan, pp. 3044\u20133050 (2020)","DOI":"10.24963\/ijcai.2020\/421"},{"issue":"3","key":"36_CR20","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1109\/TEVC.2012.2202241","volume":"17","author":"D Sudholt","year":"2012","unstructured":"Sudholt, D.: A new method for lower bounds on the running time of evolutionary algorithms. IEEE Trans. Evol. Comput. 17(3), 418\u2013435 (2012)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"36_CR21","doi-asserted-by":"crossref","unstructured":"Wegener, I.: Methods for the analysis of evolutionary algorithms on pseudo-Boolean functions. In: Evolutionary Optimization, pp. 349\u2013369. Kluwer, Norwell (2002)","DOI":"10.1007\/0-306-48041-7_14"},{"key":"36_CR22","doi-asserted-by":"crossref","unstructured":"Yu, Y., Qian, C.: Running time analysis: convergence-based analysis reduces to switch analysis. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Sendai, Japan, pp. 2603\u20132610 (2015)","DOI":"10.1109\/CEC.2015.7257209"},{"issue":"6","key":"36_CR23","doi-asserted-by":"publisher","first-page":"777","DOI":"10.1109\/TEVC.2014.2378891","volume":"19","author":"Y Yu","year":"2014","unstructured":"Yu, Y., Qian, C., Zhou, Z.H.: Switch analysis for running time analysis of evolutionary algorithms. IEEE Trans. Evol. Comput. 19(6), 777\u2013792 (2014)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"36_CR24","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1016\/j.ins.2018.06.073","volume":"465","author":"J Zhang","year":"2018","unstructured":"Zhang, J., Zhou, A., Tang, K., Zhang, G.: Preselection via classification: a case study on evolutionary multiobjective optimization. Inf. Sci. 465, 388\u2013403 (2018)","journal-title":"Inf. Sci."},{"key":"36_CR25","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-5956-9","volume-title":"Evolutionary Learning: Advances in Theories and Algorithms","author":"ZH Zhou","year":"2019","unstructured":"Zhou, Z.H., Yu, Y., Qian, C.: Evolutionary Learning: Advances in Theories and Algorithms. Springer, Singapore (2019). https:\/\/doi.org\/10.1007\/978-981-13-5956-9"}],"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\/978-3-031-14721-0_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T16:02:32Z","timestamp":1710259352000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-14721-0_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031147203","9783031147210"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-14721-0_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"15 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)"}}]}}