{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T04:46:11Z","timestamp":1748493971803,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030870126"},{"type":"electronic","value":"9783030870133"}],"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\/978-3-030-87013-3_16","type":"book-chapter","created":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T14:08:05Z","timestamp":1631196485000},"page":"205-219","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Reinforcement Learning Based Whale Optimizer"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0426-0144","authenticated-orcid":false,"given":"Marcelo","family":"Becerra-Rozas","sequence":"first","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\/0000-0001-5500-0188","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\/0000-0002-2911-8139","authenticated-orcid":false,"given":"Andr\u00e9s Trujillo","family":"Embry","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6534-2309","authenticated-orcid":false,"given":"M\u00e1ximo Arnao","family":"Molina","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0603-3722","authenticated-orcid":false,"given":"Diego","family":"Tapia","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\/0000-0002-3556-9331","authenticated-orcid":false,"given":"Sanjay","family":"Misra","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0377-4397","authenticated-orcid":false,"given":"Jos\u00e9-Miguel","family":"Rubio","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,10]]},"reference":[{"key":"16_CR1","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/978-1-4842-4470-8_7","volume-title":"Building Machine Learning and Deep Learning Models on Google Cloud Platform","author":"E Bisong","year":"2019","unstructured":"Bisong, E.: Google colaboratory. In: Bisong, E. (ed.) Building Machine Learning and Deep Learning Models on Google Cloud Platform, pp. 59\u201364. Springer, Heidelberg (2019). https:\/\/doi.org\/10.1007\/978-1-4842-4470-8_7"},{"key":"16_CR2","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1007\/978-3-030-73603-3_7","volume-title":"Innovations in Bio-Inspired Computing and Applications","author":"F Cisternas-Caneo","year":"2021","unstructured":"Cisternas-Caneo, F., et al.: A data-driven dynamic discretization framework to solve combinatorial problems using continuous metaheuristics. In: Abraham, A., Sasaki, H., Rios, R., Gandhi, N., Singh, U., Ma, K. (eds.) IBICA 2020. AISC, vol. 1372, pp. 76\u201385. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-73603-3_7"},{"key":"16_CR3","unstructured":"Crawford, B., Le\u00f3n de la Barra, C.: Los algoritmos ambidiestros (2020). https:\/\/www.mercuriovalpo.cl\/impresa\/2020\/07\/13\/full\/cuerpo-principal\/15\/. Acceded 12 Feb 2021"},{"key":"16_CR4","doi-asserted-by":"publisher","first-page":"147596","DOI":"10.1109\/ACCESS.2019.2946664","volume":"7","author":"K Hussain","year":"2019","unstructured":"Hussain, K., Zhu, W., Salleh, M.N.M.: Long-term memory Harris\u2019 hawk optimization for high dimensional and optimal power flow problems. IEEE Access 7, 147596\u2013147616 (2019)","journal-title":"IEEE Access"},{"key":"16_CR5","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.eswa.2016.10.054","volume":"70","author":"JM Lanza-Gutierrez","year":"2017","unstructured":"Lanza-Gutierrez, J.M., Crawford, B., Soto, R., Berrios, N., Gomez-Pulido, J.A., Paredes, F.: Analyzing the effects of binarization techniques when solving the set covering problem through swarm optimization. Expert Syst. Appl. 70, 67\u201382 (2017)","journal-title":"Expert Syst. Appl."},{"key":"16_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1007\/978-3-030-58817-5_65","volume-title":"Computational Science and Its Applications \u2013 ICCSA 2020","author":"J Lemus-Romani","year":"2020","unstructured":"Lemus-Romani, J., et al.: Ambidextrous socio-cultural algorithms. In: Gervasi, O., et al. (eds.) ICCSA 2020. LNCS, vol. 12254, pp. 923\u2013938. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58817-5_65"},{"key":"16_CR7","doi-asserted-by":"crossref","unstructured":"Mann, H.B., Whitney, D.R.: On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 50\u201360 (1947)","DOI":"10.1214\/aoms\/1177730491"},{"key":"16_CR8","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","volume":"95","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51\u201367 (2016)","journal-title":"Adv. Eng. Softw."},{"key":"16_CR9","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1007\/978-3-030-69143-1_55","volume-title":"Information and Communication Technology and Applications","author":"S Misra","year":"2021","unstructured":"Misra, S.: A step by step guide for choosing project topics and writing research papers in ICT related disciplines. In: ICTA 2020. CCIS, vol. 1350, pp. 727\u2013744. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-69143-1_55"},{"key":"16_CR10","doi-asserted-by":"crossref","unstructured":"Morales-Casta\u00f1eda, B., Zaldivar, D., Cuevas, E., Fausto, F., Rodr\u00edguez, A.: A better balance in metaheuristic algorithms: does it exist? Swarm Evol. Comput. 100671 (2020)","DOI":"10.1016\/j.swevo.2020.100671"},{"issue":"2","key":"16_CR11","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). https:\/\/doi.org\/10.1007\/s13748-019-00185-z","journal-title":"Progress Artif. Intell."},{"issue":"1","key":"16_CR12","first-page":"9","volume":"3","author":"RS Sutton","year":"1988","unstructured":"Sutton, R.S.: Learning to predict by the methods of temporal differences. Mach. Learn. 3(1), 9\u201344 (1988)","journal-title":"Mach. Learn."},{"key":"16_CR13","volume-title":"Reinforcement Learning: An Introduction","author":"RS Sutton","year":"2018","unstructured":"Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (2018)"},{"key":"16_CR14","unstructured":"Sutton, R.: Generalization in reinforcement learning: successful examples using sparse coarse coding. In: Advances in Neural Information Processing Systems, vol. 8 (1996)"},{"key":"16_CR15","doi-asserted-by":"publisher","DOI":"10.1002\/9780470496916","volume-title":"Metaheuristics: From Design to Implementation","author":"EG Talbi","year":"2009","unstructured":"Talbi, E.G.: Metaheuristics: From Design to Implementation, vol. 74. Wiley, Hoboken (2009)"},{"key":"16_CR16","unstructured":"Talbi, E.G.: Machine learning into metaheuristics: a survey and taxonomy of data-driven metaheuristics (2020)"},{"key":"16_CR17","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1007\/978-3-030-61702-8_2","volume-title":"Applied Informatics","author":"D Tapia","year":"2020","unstructured":"Tapia, D., et al.: A Q-learning hyperheuristic binarization framework to balance exploration and exploitation. In: Florez, H., Misra, S. (eds.) ICAI 2020. CCIS, vol. 1277, pp. 14\u201328. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-61702-8_2"},{"key":"16_CR18","doi-asserted-by":"crossref","unstructured":"Tapia, D., et al.: Embedding q-learning in the selection of metaheuristic operators: the enhanced binary grey wolf optimizar case. In: Proceeding of 2021 IEEE International Conference on Automation\/XXIV Congress of the Chilean Association of Automatic Control (ICA-ACCA), IEEE ICA\/ACCA 2021, Article in Press (2021)","DOI":"10.1109\/ICAACCA51523.2021.9465259"},{"key":"16_CR19","doi-asserted-by":"crossref","unstructured":"Taylor, M.E., Stone, P., Liu, Y.: Transfer learning via inter-task mappings for temporal difference learning. J. Mach. Learn. Res. 8(9) (2007)","DOI":"10.1145\/1329125.1329170"},{"key":"16_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1007\/978-3-030-24308-1_42","volume-title":"Computational Science and Its Applications \u2013 ICCSA 2019","author":"S Valdivia","year":"2019","unstructured":"Valdivia, S., et al.: Bridges reinforcement through conversion of tied-arch using crow search algorithm. In: Misra, S., et al. (eds.) ICCSA 2019. LNCS, vol. 11623, pp. 525\u2013535. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-24308-1_42"},{"key":"16_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1007\/978-3-030-24308-1_10","volume-title":"Computational Science and Its Applications \u2013 ICCSA 2019","author":"C V\u00e1squez","year":"2019","unstructured":"V\u00e1squez, C., et al.: Galactic swarm optimization applied to reinforcement of bridges by conversion in cable-stayed arch. In: Misra, S., et al. (eds.) ICCSA 2019. LNCS, vol. 11623, pp. 108\u2013119. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-24308-1_10"},{"key":"16_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1007\/978-3-030-58817-5_38","volume-title":"Computational Science and Its Applications \u2013 ICCSA 2020","author":"C V\u00e1squez","year":"2020","unstructured":"V\u00e1squez, C., et al.: Solving the 0\/1 Knapsack problem using a galactic swarm optimization with data-driven binarization approaches. In: Gervasi, O., et al. (eds.) ICCSA 2020. LNCS, vol. 12254, pp. 511\u2013526. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58817-5_38"},{"issue":"2","key":"16_CR23","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1109\/MCI.2009.932261","volume":"4","author":"FY Wang","year":"2009","unstructured":"Wang, F.Y., Zhang, H., Liu, D.: Adaptive dynamic programming: an introduction. IEEE Comput. Intell. Mag. 4(2), 39\u201347 (2009)","journal-title":"IEEE Comput. Intell. Mag."},{"issue":"14","key":"16_CR24","doi-asserted-by":"publisher","first-page":"10007","DOI":"10.1007\/s00521-019-04527-9","volume":"32","author":"Y Xu","year":"2019","unstructured":"Xu, Y., Pi, D.: A reinforcement learning-based communication topology in particle swarm optimization. Neural Comput. Appl. 32(14), 10007\u201310032 (2019). https:\/\/doi.org\/10.1007\/s00521-019-04527-9","journal-title":"Neural Comput. Appl."},{"issue":"2","key":"16_CR25","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1109\/TNNLS.2014.2371046","volume":"26","author":"D Zhao","year":"2014","unstructured":"Zhao, D., Zhu, Y.: MEC-a near-optimal online reinforcement learning algorithm for continuous deterministic systems. IEEE Trans. Neural Netw. Learn. Syst. 26(2), 346\u2013356 (2014)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"12","key":"16_CR26","doi-asserted-by":"publisher","first-page":"1339","DOI":"10.1049\/iet-cta.2015.0769","volume":"10","author":"Y Zhu","year":"2016","unstructured":"Zhu, Y., Zhao, D., Li, X.: Using reinforcement learning techniques to solve continuous-time non-linear optimal tracking problem without system dynamics. IET Control Theory Appl. 10(12), 1339\u20131347 (2016)","journal-title":"IET Control Theory Appl."}],"container-title":["Lecture Notes in Computer Science","Computational Science and Its Applications \u2013 ICCSA 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87013-3_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T14:24:50Z","timestamp":1631197490000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87013-3_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030870126","9783030870133"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87013-3_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"10 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCSA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science and Its Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cagliari","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccsa2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccsa.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Customed version of CyberChair 4","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1588","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":"466","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":"18","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":"29% - 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":"2,5","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":"8","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)"}}]}}