{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T09:34:13Z","timestamp":1769074453956,"version":"3.49.0"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030617011","type":"print"},{"value":"9783030617028","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-61702-8_2","type":"book-chapter","created":{"date-parts":[[2020,10,18]],"date-time":"2020-10-18T23:02:31Z","timestamp":1603062151000},"page":"14-28","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["A Q-Learning Hyperheuristic Binarization Framework to Balance Exploration and Exploitation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0603-3722","authenticated-orcid":false,"given":"Diego","family":"Tapia","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5500-0188","authenticated-orcid":false,"given":"Broderick","family":"Crawford","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5755-6929","authenticated-orcid":false,"given":"Ricardo","family":"Soto","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7723-7012","authenticated-orcid":false,"given":"Felipe","family":"Cisternas-Caneo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5379-0315","authenticated-orcid":false,"given":"Jos\u00e9","family":"Lemus-Romani","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7804-6381","authenticated-orcid":false,"given":"Mauricio","family":"Castillo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3126-8352","authenticated-orcid":false,"given":"Jos\u00e9","family":"Garc\u00eda","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7232-0412","authenticated-orcid":false,"given":"Wenceslao","family":"Palma","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0223-6052","authenticated-orcid":false,"given":"Fernando","family":"Paredes","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3556-9331","authenticated-orcid":false,"given":"Sanjay","family":"Misra","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,19]]},"reference":[{"issue":"11","key":"2_CR1","doi-asserted-by":"publisher","first-page":"1069","DOI":"10.1057\/jors.1990.166","volume":"41","author":"JE Beasley","year":"1990","unstructured":"Beasley, J.E.: Or-library: distributing test problems by electronic mail. J. Oper. Res. Soc. 41(11), 1069\u20131072 (1990). \nhttp:\/\/www.jstor.org\/stable\/2582903","journal-title":"J. Oper. Res. Soc."},{"key":"2_CR2","unstructured":"Bishop, C.M.: Pattern Recoginiton and Machine Learning (2006)"},{"issue":"3","key":"2_CR3","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1145\/937503.937505","volume":"35","author":"C Blum","year":"2003","unstructured":"Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput. Surv. 35(3), 268\u2013308 (2003). \nhttps:\/\/doi.org\/10.1145\/937503.937505","journal-title":"ACM Comput. Surv."},{"issue":"2","key":"2_CR4","doi-asserted-by":"publisher","first-page":"898","DOI":"10.1090\/s0273-0979-1980-14848-x","volume":"3","author":"RV Book","year":"1980","unstructured":"Book, R.V.: Book review: computers and intractability: a guide to the theory of NP-completeness. Bull. Am. Math. Soc. 3(2), 898\u2013905 (1980). \nhttps:\/\/doi.org\/10.1090\/s0273-0979-1980-14848-x","journal-title":"Bull. Am. Math. Soc."},{"key":"2_CR5","doi-asserted-by":"publisher","unstructured":"Burke, E., Kendall, G., Newall, J., Hart, E., Ross, P., Schulenburg, S.: Hyper-heuristics: an emerging direction in modern search technology. In: Glover, F., Kochenberger, G.A. (eds) Handbook of Metaheuristics. Springer, Boston(2006). \nhttps:\/\/doi.org\/10.1007\/0-306-48056-5_16","DOI":"10.1007\/0-306-48056-5_16"},{"key":"2_CR6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24211-8","volume-title":"Unsupervised Learning Algorithms","year":"2016","unstructured":"Celebi, M.E., Aydin, K. (eds.): Unsupervised Learning Algorithms. Springer, Cham (2016). \nhttps:\/\/doi.org\/10.1007\/978-3-319-24211-8"},{"key":"2_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.01.005","author":"SS Choong","year":"2018","unstructured":"Choong, S.S., Wong, L.P., Lim, C.P.: Automatic design of hyper-heuristic based on reinforcement learning. Inf. Sci. (NY). (2018). \nhttps:\/\/doi.org\/10.1016\/j.ins.2018.01.005","journal-title":"Inf. Sci. (NY)."},{"key":"2_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1007\/3-540-44629-X_11","volume-title":"Practice and Theory of Automated Timetabling III","author":"P Cowling","year":"2001","unstructured":"Cowling, P., Kendall, G., Soubeiga, E.: A hyperheuristic approach to scheduling a sales summit. In: Burke, E., Erben, W. (eds.) PATAT 2000. LNCS, vol. 2079, pp. 176\u2013190. Springer, Heidelberg (2001). \nhttps:\/\/doi.org\/10.1007\/3-540-44629-X_11"},{"key":"2_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1007\/978-3-319-91641-5_8","volume-title":"Bioinspired Optimization Methods and Their Applications","author":"B Crawford","year":"2018","unstructured":"Crawford, B., Soto, R., Astorga, G., Garc\u00eda, J.: Constructive metaheuristics for the set covering problem. In: Koro\u0161ec, P., Melab, N., Talbi, E.-G. (eds.) BIOMA 2018. LNCS, vol. 10835, pp. 88\u201399. Springer, Cham (2018). \nhttps:\/\/doi.org\/10.1007\/978-3-319-91641-5_8"},{"key":"2_CR10","doi-asserted-by":"publisher","unstructured":"Crawford, B., Soto, R., Astorga, G., Garc\u00eda, J., Castro, C., Paredes, F.: Putting continuous metaheuristics to work in binary search spaces (2017). \nhttps:\/\/doi.org\/10.1155\/2017\/8404231","DOI":"10.1155\/2017\/8404231"},{"issue":"3","key":"2_CR11","doi-asserted-by":"publisher","first-page":"1033","DOI":"10.1051\/ro\/2019039","volume":"53","author":"B Crawford","year":"2019","unstructured":"Crawford, B., Soto, R., Olivares, R., Riquelme, L., Astorga, G., Johnson, F., Cort\u00e9s, E., Castro, C., Paredes, F.: A self-adaptive biogeography-based algorithm to solve the set covering problem. RAIRO - Oper. Res. 53(3), 1033\u20131059 (2019). \nhttps:\/\/doi.org\/10.1051\/ro\/2019039","journal-title":"RAIRO - Oper. Res."},{"key":"2_CR12","doi-asserted-by":"publisher","DOI":"10.1109\/CI-M.2006.248054","author":"M Dorigo","year":"2006","unstructured":"Dorigo, M., Birattari, M., St\u00fctzle, T.: Ant colony optimization artificial ants as a computational intelligence technique. IEEE Comput. Intell. Mag. (2006). \nhttps:\/\/doi.org\/10.1109\/CI-M.2006.248054","journal-title":"IEEE Comput. Intell. Mag."},{"key":"2_CR13","doi-asserted-by":"publisher","DOI":"10.1109\/4235.585892","author":"M Dorigo","year":"1997","unstructured":"Dorigo, M., Gambardella, L.M.: Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. (1997). \nhttps:\/\/doi.org\/10.1109\/4235.585892","journal-title":"IEEE Trans. Evol. Comput."},{"key":"2_CR14","unstructured":"Dorigo, M., Maniezzo, V., Colorni, A., Dorigo, M.: Positive Feedback as a Search Strategy. Technical report, 91-016 (1991)"},{"key":"2_CR15","doi-asserted-by":"publisher","DOI":"10.1080\/03052150500384759","author":"M Eusuff","year":"2006","unstructured":"Eusuff, M., Lansey, K., Pasha, F.: Shuffled frog-leaping algorithm: A memetic meta-heuristic for discrete optimization. Eng. Optim. (2006). \nhttps:\/\/doi.org\/10.1080\/03052150500384759","journal-title":"Eng. Optim."},{"key":"2_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/0167-6377(89)90002-3","author":"TA Feo","year":"1989","unstructured":"Feo, T.A., Resende, M.G.: A probabilistic heuristic for a computationally difficult set covering problem. Oper. Res. Lett. (1989). \nhttps:\/\/doi.org\/10.1016\/0167-6377(89)90002-3","journal-title":"Oper. Res. Lett."},{"key":"2_CR17","doi-asserted-by":"publisher","DOI":"10.1287\/ijoc.2.1.4","author":"F Glover","year":"1990","unstructured":"Glover, F.: Tabu search-Part II. ORSA J. Comput. (1990). \nhttps:\/\/doi.org\/10.1287\/ijoc.2.1.4","journal-title":"ORSA J. Comput."},{"key":"2_CR18","doi-asserted-by":"publisher","DOI":"10.1038\/scientificamerican0792-66","author":"JH Holland","year":"1992","unstructured":"Holland, J.H.: Genetic algorithms. Sci. Am. (1992). \nhttps:\/\/doi.org\/10.1038\/scientificamerican0792-66","journal-title":"Sci. Am."},{"issue":"4","key":"2_CR19","doi-asserted-by":"publisher","first-page":"2191","DOI":"10.1007\/s10462-017-9605-z","volume":"52","author":"K Hussain","year":"2018","unstructured":"Hussain, K., Mohd Salleh, M.N., Cheng, S., Shi, Y.: Metaheuristic research: a comprehensive survey. Artif. Intell. Rev. 52(4), 2191\u20132233 (2018). \nhttps:\/\/doi.org\/10.1007\/s10462-017-9605-z","journal-title":"Artif. Intell. Rev."},{"key":"2_CR20","doi-asserted-by":"publisher","unstructured":"Khamassi, I., Hammami, M., Gh\u00e9dira, K.: Ant-Q hyper-heuristic approach for solving 2-dimensional Cutting Stock Problem. In: IEEE SSCI 2011 - Symposium Series Computing Intelligent - SIS 2011 2011 IEEE Symposium Swarm Intelligent (2011). \nhttps:\/\/doi.org\/10.1109\/SIS.2011.5952530","DOI":"10.1109\/SIS.2011.5952530"},{"key":"2_CR21","doi-asserted-by":"publisher","unstructured":"Kotsiantis, S.B.: Supervised machine learning: a review of classification techniques. In: Proceedings of the 2007 Conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in EHealth, HCI, Information Retrieval and Pervasive Technologies, pp. 3\u201324. IOS Press, NLD (2007). \nhttps:\/\/doi.org\/10.5555\/1566770.1566773","DOI":"10.5555\/1566770.1566773"},{"key":"2_CR22","doi-asserted-by":"publisher","unstructured":"Leguizamon, G., Michalewicz, Z.: A new version of ant system for subset problems. In: Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999 (1999). \nhttps:\/\/doi.org\/10.1109\/CEC.1999.782655","DOI":"10.1109\/CEC.1999.782655"},{"key":"2_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/s10710-011-9139-0","author":"M Lones","year":"2011","unstructured":"Lones, M.: Sean Luke: essentials of metaheuristics. Genet. Program Evolvable Mach. (2011). \nhttps:\/\/doi.org\/10.1007\/s10710-011-9139-0","journal-title":"Genet. Program Evolvable Mach."},{"key":"2_CR24","doi-asserted-by":"publisher","unstructured":"Mafarja, M., Eleyan, D., Abdullah, S., Mirjalili, S.: S-shaped vs. V-shaped transfer functions for ant lion optimization algorithm in feature selection problem. In: ACM International Conference Proceedings Series (2017). \nhttps:\/\/doi.org\/10.1145\/3102304.3102325","DOI":"10.1145\/3102304.3102325"},{"key":"2_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2013.12.007","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey Wolf Optimizer. Adv. Eng. Softw. (2014). \nhttps:\/\/doi.org\/10.1016\/j.advengsoft.2013.12.007","journal-title":"Adv. Eng. Softw."},{"key":"2_CR26","doi-asserted-by":"publisher","unstructured":"Mirjalili, S., Song Dong, J., Lewis, A.: Nature-Inspired Optimizers (2020).\nhttps:\/\/doi.org\/10.1007\/978-3-030-12127-3","DOI":"10.1007\/978-3-030-12127-3"},{"key":"2_CR27","doi-asserted-by":"publisher","unstructured":"M\u00fcller, A.C., Guido, S.: Introduction to Machine Learning with Python: a guide for data scientists (2016). \nhttps:\/\/doi.org\/10.1017\/CBO9781107415324.004","DOI":"10.1017\/CBO9781107415324.004"},{"key":"2_CR28","doi-asserted-by":"publisher","unstructured":"Muncie, H.L., Sobal, J., DeForge, B.: Research methodologies (1989). \nhttps:\/\/doi.org\/10.5040\/9781350004900.0008","DOI":"10.5040\/9781350004900.0008"},{"key":"2_CR29","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2002.802449","author":"C Solnon","year":"2002","unstructured":"Solnon, C.: Ants can solve constraint satisfaction problems. IEEE Trans. Evol. Comput. (2002). \nhttps:\/\/doi.org\/10.1109\/TEVC.2002.802449","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"2","key":"2_CR30","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/s13748-019-00185-z","volume":"8","author":"H Song","year":"2019","unstructured":"Song, H., Triguero, I., \u00d6zcan, E.: A review on the self and dual interactions between machine learning and optimisation. Progress Artif. Intell 8(2), 143\u2013165 (2019). \nhttps:\/\/doi.org\/10.1007\/s13748-019-00185-z","journal-title":"Progress Artif. Intell"},{"key":"2_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-739X(00)00043-1","author":"T St\u00fctzle","year":"2000","unstructured":"St\u00fctzle, T., Hoos, H.H.: MAX-MIN ant system. Futur. Gener. Comput. Syst. (2000). \nhttps:\/\/doi.org\/10.1016\/S0167-739X(00)00043-1","journal-title":"Futur. Gener. Comput. Syst."},{"key":"2_CR32","doi-asserted-by":"publisher","unstructured":"Sutton, R.S., Barto, A.G.: Reinforcement learning: an introduction 2018. Technical report (2017). \nhttps:\/\/doi.org\/10.1109\/TNN.1998.712192","DOI":"10.1109\/TNN.1998.712192"},{"key":"2_CR33","doi-asserted-by":"publisher","unstructured":"Talbi, E.G.: Metaheuristics: From Design to Implementation (2009). \nhttps:\/\/doi.org\/10.1002\/9780470496916","DOI":"10.1002\/9780470496916"},{"key":"2_CR34","unstructured":"Talbi, E.G.: Machine learning into metaheuristics: a survey and taxonomy of data-driven metaheuristics, June 2020. \nhttps:\/\/hal.inria.fr\/hal-02745295\n\n, working paper or preprint"},{"key":"2_CR35","doi-asserted-by":"publisher","DOI":"10.1023\/A:1022676722315","author":"CJ Watkins","year":"1992","unstructured":"Watkins, C.J., Dayan, P.: Technical note: Q-learning. Mach. Learn. (1992). \nhttps:\/\/doi.org\/10.1023\/A:1022676722315","journal-title":"Mach. Learn."}],"container-title":["Communications in Computer and Information Science","Applied Informatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-61702-8_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,10,18]],"date-time":"2020-10-18T23:07:32Z","timestamp":1603062452000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-61702-8_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030617011","9783030617028"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-61702-8_2","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"19 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Applied Informatics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ota","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nigeria","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icai22020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/icai.itiud.org","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":"101","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":"35","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":"35% - 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":"3","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)"}}]}}