{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T16:00:32Z","timestamp":1756310432669,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031340192"},{"type":"electronic","value":"9783031340208"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-34020-8_20","type":"book-chapter","created":{"date-parts":[[2023,5,26]],"date-time":"2023-05-26T10:02:30Z","timestamp":1685095350000},"page":"263-276","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Binary Black Widow with Hill Climbing Algorithm for Feature Selection"],"prefix":"10.1007","author":[{"given":"Ahmed","family":"Al-saedi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdul-Rahman","family":"Mawlood-Yunis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,27]]},"reference":[{"key":"20_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.112824","volume":"139","author":"M Abdel-Basset","year":"2020","unstructured":"Abdel-Basset, M., El-Shahat, D., El-henawy, I., de Albuquerque, V.H.C., Mirjalili, S.: A new fusion of grey wolf optimizer algorithm with a two-phase mutation for feature selection. Expert Syst. Appl. 139, 112824 (2020)","journal-title":"Expert Syst. Appl."},{"issue":"12","key":"20_CR2","doi-asserted-by":"publisher","first-page":"3445","DOI":"10.1007\/s13042-019-00931-8","volume":"10","author":"N Al-Madi","year":"2019","unstructured":"Al-Madi, N., Faris, H., Mirjalili, S.: Binary multi-verse optimization algorithm for global optimization and discrete problems. Int. J. Mach. Learn. Cybern. 10(12), 3445\u20133465 (2019). https:\/\/doi.org\/10.1007\/s13042-019-00931-8","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"20_CR3","doi-asserted-by":"publisher","unstructured":"Al-Saedi, A., Mawlood-Yunis, A.R.: Binary black widow optimization algorithm for feature selection problems. In: Simos, D.E., Rasskazova, V.A., Archetti, F., Kotsireas, I.S., Pardalos, P.M. (eds.) Learning and Intelligent Optimization (LION 2022). LNCS, vol. 13621, pp. 93\u2013107. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-24866-5_7","DOI":"10.1007\/978-3-031-24866-5_7"},{"issue":"1","key":"20_CR4","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1007\/s00500-020-05164-4","volume":"25","author":"M Alweshah","year":"2020","unstructured":"Alweshah, M., Alkhalaileh, S., Albashish, D., Mafarja, M., Bsoul, Q., Dorgham, O.: A hybrid mine blast algorithm for feature selection problems. Soft. Comput. 25(1), 517\u2013534 (2020). https:\/\/doi.org\/10.1007\/s00500-020-05164-4","journal-title":"Soft. Comput."},{"key":"20_CR5","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.eswa.2018.08.051","volume":"116","author":"S Arora","year":"2019","unstructured":"Arora, S., Anand, P.: Binary butterfly optimization approaches for feature selection. Expert Syst. Appl. 116, 147\u2013160 (2019)","journal-title":"Expert Syst. Appl."},{"issue":"4","key":"20_CR6","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.icte.2017.08.001","volume":"4","author":"N Dordaie","year":"2018","unstructured":"Dordaie, N., Navimipour, N.J.: A hybrid particle swarm optimization and hill climbing algorithm for task scheduling in the cloud environments. ICT Express 4(4), 199\u2013202 (2018)","journal-title":"ICT Express"},{"key":"20_CR7","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1016\/j.neucom.2015.06.083","volume":"172","author":"E Emary","year":"2016","unstructured":"Emary, E., Zawbaa, H.M., Hassanien, A.E.: Binary grey wolf optimization approaches for feature selection. Neurocomputing 172, 371\u2013381 (2016)","journal-title":"Neurocomputing"},{"key":"20_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106131","volume":"203","author":"AI Hammouri","year":"2020","unstructured":"Hammouri, A.I., Mafarja, M., Al-Betar, M.A., Awadallah, M.A., Abu-Doush, I.: An improved dragonfly algorithm for feature selection. Knowl.-Based Syst. 203, 106131 (2020)","journal-title":"Knowl.-Based Syst."},{"key":"20_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2019.103249","volume":"87","author":"V Hayyolalam","year":"2020","unstructured":"Hayyolalam, V., Kazem, A.A.P.: Black widow optimization algorithm: a novel meta-heuristic approach for solving engineering optimization problems. Eng. Appl. Artif. Intell. 87, 103249 (2020)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"20_CR10","doi-asserted-by":"crossref","unstructured":"Mafarja, M., Jarrar, R., Ahmad, S., Abusnaina, A.A.: Feature selection using binary particle swarm optimization with time varying inertia weight strategies. In: Proceedings of the 2nd International Conference on Future Networks and Distributed Systems, pp. 1\u20139 (2018)","DOI":"10.1145\/3231053.3231071"},{"key":"20_CR11","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.asoc.2017.11.006","volume":"62","author":"M Mafarja","year":"2018","unstructured":"Mafarja, M., Mirjalili, S.: Whale optimization approaches for wrapper feature selection. Appl. Soft Comput. 62, 441\u2013453 (2018)","journal-title":"Appl. Soft Comput."},{"key":"20_CR12","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.advengsoft.2015.01.010","volume":"83","author":"S Mirjalili","year":"2015","unstructured":"Mirjalili, S.: The ant lion optimizer. Adv. Eng. Softw. 83, 80\u201398 (2015)","journal-title":"Adv. Eng. Softw."},{"issue":"3","key":"20_CR13","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1007\/s00521-013-1525-5","volume":"25","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili, S., Mirjalili, S.M., Yang, X.S.: Binary bat algorithm. Neural Comput. Appl. 25(3), 663\u2013681 (2014)","journal-title":"Neural Comput. Appl."},{"key":"20_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108743","volume":"246","author":"RR Mostafa","year":"2022","unstructured":"Mostafa, R.R., Ewees, A.A., Ghoniem, R.M., Abualigah, L., Hashim, F.A.: Boosting chameleon swarm algorithm with consumption AEO operator for global optimization and feature selection. Knowl.-Based Syst. 246, 108743 (2022)","journal-title":"Knowl.-Based Syst."},{"key":"20_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113364","volume":"152","author":"N Neggaz","year":"2020","unstructured":"Neggaz, N., Houssein, E.H., Hussain, K.: An efficient henry gas solubility optimization for feature selection. Expert Syst. Appl. 152, 113364 (2020)","journal-title":"Expert Syst. Appl."},{"key":"20_CR16","doi-asserted-by":"publisher","unstructured":"Syriopoulos, P.K., Kotsiantis, S.B., Vrahatis, M.N.: Survey on KNN methods in data science. In: Simos, D.E., Rasskazova, V.A., Archetti, F., Kotsireas, I.S., Pardalos, P.M. (eds.) Learning and Intelligent Optimization (LION 2022). LNCS, vol. 13621, pp. 379\u2013393. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-24866-5_28","DOI":"10.1007\/978-3-031-24866-5_28"},{"issue":"1","key":"20_CR17","first-page":"3","volume":"19","author":"B Venkatesh","year":"2020","unstructured":"Venkatesh, B., Anuradha, J.: A review of feature selection and its methods. Cybern. Inf. Technol. 19(1), 3\u201326 (2020)","journal-title":"Cybern. Inf. Technol."},{"key":"20_CR18","doi-asserted-by":"publisher","unstructured":"Yang, X.S.: A new metaheuristic bat-inspired algorithm. In: Gonz\u00e1ilez, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). Studies in Computational Intelligence, vol. 284, pp. 65\u201374. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-12538-6_6","DOI":"10.1007\/978-3-642-12538-6_6"},{"key":"20_CR19","doi-asserted-by":"crossref","unstructured":"Zawbaa, H.M., Emary, E., Parv, B., Sharawi, M.: Feature selection approach based on moth-flame optimization algorithm. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 4612\u20134617 (2016)","DOI":"10.1109\/CEC.2016.7744378"}],"container-title":["Communications in Computer and Information Science","Optimization and Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-34020-8_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,26]],"date-time":"2023-05-26T10:09:59Z","timestamp":1685095799000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-34020-8_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031340192","9783031340208"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-34020-8_20","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"27 May 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"OLA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Optimization and Learning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Malaga","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 May 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 May 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ola2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ola2023.sciencesconf.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Open","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"sciencesconf.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"78","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":"32","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":"41% - 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","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)"}}]}}