{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T17:41:59Z","timestamp":1769190119787,"version":"3.49.0"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030917012","type":"print"},{"value":"9783030917029","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-91702-9_19","type":"book-chapter","created":{"date-parts":[[2021,11,27]],"date-time":"2021-11-27T20:02:46Z","timestamp":1638043366000},"page":"280-294","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Online Selection of Heuristic Operators with Deep Q-Network: A Study on the HyFlex Framework"],"prefix":"10.1007","author":[{"given":"Augusto","family":"Dantas","sequence":"first","affiliation":[]},{"given":"Aurora","family":"Pozo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,11,28]]},"reference":[{"issue":"2","key":"19_CR1","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1023\/A:1013689704352","volume":"47","author":"P Auer","year":"2002","unstructured":"Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite-time analysis of the multiarmed bandit problem. Mach. Learn. 47(2), 235\u2013256 (2002)","journal-title":"Mach. Learn."},{"issue":"6","key":"19_CR2","doi-asserted-by":"publisher","first-page":"4135","DOI":"10.1016\/j.asoc.2011.02.032","volume":"11","author":"C Blum","year":"2011","unstructured":"Blum, C., Puchinger, J., Raidl, G.R., Roli, A.: Hybrid metaheuristics in combinatorial optimization: a survey. Appl. Soft Comput. 11(6), 4135\u20134151 (2011)","journal-title":"Appl. Soft Comput."},{"key":"19_CR3","doi-asserted-by":"publisher","unstructured":"Burke, E.K., Hyde, M., Kendall, G., Ochoa, G., \u00d6zcan, E., Woodward, J.R.: A Classification of Hyper-heuristic Approaches, pp. 449\u2013468. Springer, US, Boston, MA (2010). https:\/\/doi.org\/10.1007\/978-1-4419-1665-5_15","DOI":"10.1007\/978-1-4419-1665-5_15"},{"key":"19_CR4","doi-asserted-by":"crossref","unstructured":"Buzdalova, A., Kononov, V., Buzdalov, M.: Selecting evolutionary operators using reinforcement learning: initial explorations. In: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, pp. 1033\u20131036 (2014)","DOI":"10.1145\/2598394.2605681"},{"key":"19_CR5","doi-asserted-by":"crossref","unstructured":"DaCosta, L., Fialho, A., Schoenauer, M., Sebag, M.: Adaptive operator selection with dynamic multi-armed bandits. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pp. 913\u2013920. GECCO \u201908, Association for Computing Machinery, New York, NY, USA (2008)","DOI":"10.1145\/1389095.1389272"},{"key":"19_CR6","doi-asserted-by":"crossref","unstructured":"Dantas, A., Rego, A.F.D., Pozo, A.: Using deep q-network for selection hyper-heuristics. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 1488\u20131492. GECCO \u201921, Association for Computing Machinery, New York, NY, USA (2021)","DOI":"10.1145\/3449726.3463187"},{"key":"19_CR7","unstructured":"Fialho, \u00c1.: Adaptive Operator Selection for Optimization. Universit\u00e9 Paris Sud - Paris XI (Dec, Theses (2010)"},{"key":"19_CR8","doi-asserted-by":"crossref","unstructured":"Handoko, S.D., Nguyen, D.T., Yuan, Z., Lau, H.C.: Reinforcement learning for adaptive operator selection in memetic search applied to quadratic assignment problem. In: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, pp. 193\u2013194. GECCO Comp \u201914, Association for Computing Machinery, New York, NY, USA (2014)","DOI":"10.1145\/2598394.2598451"},{"key":"19_CR9","doi-asserted-by":"crossref","unstructured":"Karimi-Mamaghan, M., Mohammadi, M., Meyer, P., Karimi-Mamaghan, A.M., Talbi, E.G.: Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art. European Journal of Operational Research (2021)","DOI":"10.1016\/j.ejor.2021.04.032"},{"issue":"1","key":"19_CR10","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1109\/TEVC.2013.2239648","volume":"18","author":"K Li","year":"2014","unstructured":"Li, K., Fialho, \u00c1., Kwong, S., Zhang, Q.: Adaptive operator selection with bandits for a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 18(1), 114\u2013130 (2014)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"2","key":"19_CR11","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1016\/j.ejor.2019.09.021","volume":"282","author":"H Mosadegh","year":"2020","unstructured":"Mosadegh, H., Ghomi, S.F., S\u00fcer, G.A.: Stochastic mixed-model assembly line sequencing problem: Mathematical modeling and q-learning based simulated annealing hyper-heuristics. Eur. J. Oper. Res. 282(2), 530\u2013544 (2020)","journal-title":"Eur. J. Oper. Res."},{"key":"19_CR12","doi-asserted-by":"crossref","unstructured":"Ochoa, G., et al.: HyFlex: A Benchmark Framework for Cross-domain Heuristic Search, vol. 7245, pp. 136\u2013147 (2012)","DOI":"10.1007\/978-3-642-29124-1_12"},{"key":"19_CR13","unstructured":"Pedregosa, F., et al.: Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)"},{"key":"19_CR14","doi-asserted-by":"crossref","unstructured":"Puterman, M.L.: Chapter 8 markov decision processes. In: Handbooks in Operations Research and Management Science, Stochastic Models, vol. 2, pp. 331\u2013434. Elsevier (1990)","DOI":"10.1016\/S0927-0507(05)80172-0"},{"key":"19_CR15","doi-asserted-by":"crossref","unstructured":"Sharma, M., Komninos, A., L\u00f3pez-Ib\u00e1\u00f1ez, M., Kazakov, D.: Deep reinforcement learning based parameter control in differential evolution. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 709\u2013717 (2019)","DOI":"10.1145\/3321707.3321813"},{"issue":"3","key":"19_CR16","doi-asserted-by":"publisher","first-page":"972","DOI":"10.1016\/j.ejor.2017.01.042","volume":"260","author":"JA Soria-Alcaraz","year":"2017","unstructured":"Soria-Alcaraz, J.A., Ochoa, G., Sotelo-Figeroa, M.A., Burke, E.K.: A methodology for determining an effective subset of heuristics in selection hyper-heuristics. Eur. J. Oper. Res. 260(3), 972\u2013983 (2017)","journal-title":"Eur. J. Oper. Res."},{"key":"19_CR17","unstructured":"Sutton, R.S., Barto, A.G.: Reinforcement Learning, Second Edition: An Introduction. MIT Press (2018)"},{"key":"19_CR18","doi-asserted-by":"publisher","unstructured":"Teng, T.-H., Handoko, S.D., Lau, H.C.: Self-organizing neural network for adaptive operator selection in evolutionary search. In: Festa, P., Sellmann, M., Vanschoren, J. (eds.) LION 2016. LNCS, vol. 10079, pp. 187\u2013202. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-50349-3_13","DOI":"10.1007\/978-3-319-50349-3_13"},{"issue":"3","key":"19_CR19","first-page":"279","volume":"8","author":"CJCH Watkins","year":"1992","unstructured":"Watkins, C.J.C.H., Dayan, P.: Q-learning. Mach. Learn. 8(3), 279\u2013292 (1992)","journal-title":"Mach. Learn."}],"container-title":["Lecture Notes in Computer Science","Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-91702-9_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T12:04:08Z","timestamp":1709813048000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-91702-9_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030917012","9783030917029"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-91702-9_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"28 November 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BRACIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazilian Conference on Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bracis2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/c4ai.inova.usp.br\/bracis\/","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":"JEMS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"192","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":"77","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":"40% - 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","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":"3.1","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to COVID-19, the conference was held as an online event.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}