{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T04:32:44Z","timestamp":1759638764415,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031041471"},{"type":"electronic","value":"9783031041488"}],"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\/978-3-031-04148-8_13","type":"book-chapter","created":{"date-parts":[[2022,4,3]],"date-time":"2022-04-03T18:02:31Z","timestamp":1649008951000},"page":"191-207","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Stagnation Detection Meets Fast Mutation"],"prefix":"10.1007","author":[{"given":"Benjamin","family":"Doerr","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amirhossein","family":"Rajabi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,4,4]]},"reference":[{"key":"13_CR1","doi-asserted-by":"crossref","unstructured":"Antipov, D., Buzdalov, M., Doerr, B.: Fast mutation in crossover-based algorithms. In: Genetic and Evolutionary Computation Conference, GECCO 2020, pp. 1268\u20131276. ACM (2020)","DOI":"10.1145\/3377930.3390172"},{"key":"13_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1007\/978-3-030-58115-2_39","volume-title":"Parallel Problem Solving from Nature \u2013 PPSN XVI","author":"D Antipov","year":"2020","unstructured":"Antipov, D., Buzdalov, M., Doerr, B.: First steps towards a runtime analysis when starting with a good solution. In: B\u00e4ck, T., et al. (eds.) PPSN 2020. LNCS, vol. 12270, pp. 560\u2013573. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58115-2_39"},{"key":"13_CR3","doi-asserted-by":"crossref","unstructured":"Antipov, D., Buzdalov, M., Doerr, B.: Lazy parameter tuning and control: choosing all parameters randomly from a power-law distribution. In: Genetic and Evolutionary Computation Conference, GECCO 2021, pp. 1115\u20131123. ACM (2021)","DOI":"10.1145\/3449639.3459377"},{"key":"13_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1007\/978-3-030-58115-2_38","volume-title":"Parallel Problem Solving from Nature \u2013 PPSN XVI","author":"D Antipov","year":"2020","unstructured":"Antipov, D., Doerr, B.: Runtime analysis of a heavy-tailed $$(1+(\\lambda ,\\lambda ))$$ genetic algorithm on jump functions. In: B\u00e4ck, T., et al. (eds.) PPSN 2020. LNCS, vol. 12270, pp. 545\u2013559. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58115-2_38"},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"Bambury, H., Bultel, A., Doerr, B.: Generalized jump functions. In: Genetic and Evolutionary Computation Conference, GECCO 2021, pp. 1124\u20131132. ACM (2021)","DOI":"10.1145\/3449639.3459367"},{"key":"13_CR6","doi-asserted-by":"crossref","unstructured":"Corus, D., Oliveto, P.S., Yazdani, D.: Automatic adaptation of hypermutation rates for multimodal optimisation. In: Foundations of Genetic Algorithms, FOGA 2021, pp. 4:1\u20134:12. ACM (2021)","DOI":"10.1145\/3450218.3477305"},{"key":"13_CR7","doi-asserted-by":"publisher","first-page":"956","DOI":"10.1109\/TEVC.2021.3068574","volume":"25","author":"D Corus","year":"2021","unstructured":"Corus, D., Oliveto, P.S., Yazdani, D.: Fast immune system-inspired hypermutation operators for combinatorial optimization. IEEE Trans. Evol. Comput. 25, 956\u2013970 (2021)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"13_CR8","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1109\/TEVC.2017.2724201","volume":"22","author":"D Dang","year":"2018","unstructured":"Dang, D., Friedrich, T., K\u00f6tzing, T., Krejca, M.S., Lehre, P.K., Oliveto, P.S., Sudholt, D., Sutton, A.M.: Escaping local optima using crossover with emergent diversity. IEEE Trans. Evol. Comput. 22, 484\u2013497 (2018)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"13_CR9","doi-asserted-by":"crossref","unstructured":"Doerr, B.: Does comma selection help to cope with local optima? In: Genetic and Evolutionary Computation Conference, GECCO 2020, pp. 1304\u20131313. ACM (2020)","DOI":"10.1145\/3377930.3389823"},{"key":"13_CR10","series-title":"Natural Computing Series","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1007\/978-3-030-29414-4_6","volume-title":"Theory of Evolutionary Computation","author":"B Doerr","year":"2020","unstructured":"Doerr, B., Doerr, C.: Theory of parameter control for discrete black-box optimization: provable performance gains through dynamic parameter choices. In: Theory of Evolutionary Computation. NCS, pp. 271\u2013321. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-29414-4_6"},{"key":"13_CR11","doi-asserted-by":"crossref","unstructured":"Doerr, B., Le, H.P., Makhmara, R., Nguyen, T.D.: Fast genetic algorithms. In: Genetic and Evolutionary Computation Conference, GECCO 2017, pp. 777\u2013784. ACM (2017)","DOI":"10.1145\/3071178.3071301"},{"key":"13_CR12","unstructured":"Doerr, B., Rajabi, A.: Stagnation detection meets fast mutation. CoRR abs\/2201.12158 (2022). https:\/\/arxiv.org\/abs\/2201.12158"},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Doerr, B., Zheng, W.: Theoretical analyses of multi-objective evolutionary algorithms on multi-modal objectives. In: Conference on Artificial Intelligence, AAAI 2021, pp. 12293\u201312301. AAAI Press (2021)","DOI":"10.1145\/3449726.3462719"},{"key":"13_CR14","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. Theoret. Comput. Sci. 276, 51\u201381 (2002)","journal-title":"Theoret. Comput. Sci."},{"key":"13_CR15","unstructured":"Friedrich, T., G\u00f6bel, A., Quinzan, F., Wagner, M.: Evolutionary algorithms and submodular functions: Benefits of heavy-tailed mutations. CoRR abs\/1805.10902 (2018)"},{"key":"13_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1007\/978-3-319-99253-2_11","volume-title":"Parallel Problem Solving from Nature \u2013 PPSN XV","author":"T Friedrich","year":"2018","unstructured":"Friedrich, T., G\u00f6bel, A., Quinzan, F., Wagner, M.: Heavy-tailed mutation operators in single-objective combinatorial optimization. In: Auger, A., Fonseca, C.M., Louren\u00e7o, N., Machado, P., Paquete, L., Whitley, D. (eds.) PPSN 2018. LNCS, vol. 11101, pp. 134\u2013145. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-99253-2_11"},{"key":"13_CR17","doi-asserted-by":"crossref","unstructured":"Friedrich, T., Quinzan, F., Wagner, M.: Escaping large deceptive basins of attraction with heavy-tailed mutation operators. In: Genetic and Evolutionary Computation Conference, GECCO 2018, pp. 293\u2013300. ACM (2018)","DOI":"10.1145\/3205455.3205515"},{"key":"13_CR18","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.tcs.2004.03.038","volume":"320","author":"A Pr\u00fcgel-Bennett","year":"2004","unstructured":"Pr\u00fcgel-Bennett, A.: When a genetic algorithm outperforms hill-climbing. Theoret. Comput. Sci. 320, 135\u2013153 (2004)","journal-title":"Theoret. Comput. Sci."},{"key":"13_CR19","doi-asserted-by":"crossref","unstructured":"Rajabi, A., Witt, C.: Self-adjusting evolutionary algorithms for multimodal optimization. In: Genetic and Evolutionary Computation Conference, GECCO 2020, pp. 1314\u20131322. ACM (2020)","DOI":"10.1145\/3377930.3389833"},{"key":"13_CR20","doi-asserted-by":"crossref","unstructured":"Rajabi, A., Witt, C.: Stagnation detection in highly multimodal fitness landscapes. In: Genetic and Evolutionary Computation Conference, GECCO 2021. pp. 1178\u20131186. ACM (2021)","DOI":"10.1145\/3449639.3459336"},{"key":"13_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1007\/978-3-030-72904-2_10","volume-title":"Evolutionary Computation in Combinatorial Optimization","author":"A Rajabi","year":"2021","unstructured":"Rajabi, A., Witt, C.: Stagnation detection with randomized local search. In: Zarges, C., Verel, S. (eds.) EvoCOP 2021. LNCS, vol. 12692, pp. 152\u2013168. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-72904-2_10"},{"key":"13_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1007\/3-540-48224-5_6","volume-title":"Automata, Languages and Programming","author":"I Wegener","year":"2001","unstructured":"Wegener, I.: Theoretical aspects of evolutionary algorithms. In: Orejas, F., Spirakis, P.G., van Leeuwen, J. (eds.) ICALP 2001. LNCS, vol. 2076, pp. 64\u201378. Springer, Heidelberg (2001). https:\/\/doi.org\/10.1007\/3-540-48224-5_6"},{"key":"13_CR23","doi-asserted-by":"crossref","unstructured":"Witt, C.: On crossing fitness valleys with majority-vote crossover and estimation-of-distribution algorithms. In: Foundations of Genetic Algorithms, FOGA 2021, pp. 2:1\u20132:15. ACM (2021)","DOI":"10.1145\/3450218.3477303"},{"key":"13_CR24","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/978-3-319-95957-3_4","volume-title":"Intelligent Computing Methodologies","author":"M Wu","year":"2018","unstructured":"Wu, M., Qian, C., Tang, K.: Dynamic mutation based pareto optimization for subset selection. In: Huang, D.-S., Gromiha, M.M., Han, K., Hussain, A. (eds.) ICIC 2018. LNCS (LNAI), vol. 10956, pp. 25\u201335. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-95957-3_4"}],"container-title":["Lecture Notes in Computer Science","Evolutionary Computation in Combinatorial Optimization"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-04148-8_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,3]],"date-time":"2022-04-03T18:06:11Z","timestamp":1649009171000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-04148-8_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031041471","9783031041488"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-04148-8_13","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":"4 April 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EvoCOP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Evolutionary Computation in Combinatorial Optimization (Part of EvoStar)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Madrid","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","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":"20 April 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 April 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"evocop2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.evostar.org\/2022\/","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":"28","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":"13","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":"4","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":"2","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)"}}]}}