{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T10:13:42Z","timestamp":1743156822019,"version":"3.40.3"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030137083"},{"type":"electronic","value":"9783030137090"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","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":[[2019]]},"DOI":"10.1007\/978-3-030-13709-0_4","type":"book-chapter","created":{"date-parts":[[2019,2,13]],"date-time":"2019-02-13T18:09:04Z","timestamp":1550081344000},"page":"38-50","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Optimization of Neural Network Training with ELM Based on the Iterative Hybridization of Differential Evolution with Local Search and Restarts"],"prefix":"10.1007","author":[{"given":"David","family":"Sotelo","sequence":"first","affiliation":[]},{"given":"Daniela","family":"Vel\u00e1squez","sequence":"additional","affiliation":[]},{"given":"Carlos","family":"Cobos","sequence":"additional","affiliation":[]},{"given":"Martha","family":"Mendoza","sequence":"additional","affiliation":[]},{"given":"Luis","family":"G\u00f3mez","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,2,14]]},"reference":[{"key":"4_CR1","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.patcog.2016.04.003","volume":"58","author":"Y Zhang","year":"2016","unstructured":"Zhang, Y., Wu, J., Cai, Z., Zhang, P., Chen, L.: Memetic extreme learning machine. Pattern Recognit. 58, 135\u2013148 (2016)","journal-title":"Pattern Recognit."},{"key":"4_CR2","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1016\/j.neucom.2013.09.016","volume":"129","author":"T Matias","year":"2014","unstructured":"Matias, T., Souza, F., Ara\u00fajo, R., Antunes, C.H.: Learning of a single-hidden layer feedforward neural network using an optimized extreme learning machine. Neurocomputing 129, 428\u2013436 (2014)","journal-title":"Neurocomputing"},{"issue":"10","key":"4_CR3","doi-asserted-by":"publisher","first-page":"1759","DOI":"10.1016\/j.patcog.2005.03.028","volume":"38","author":"Q-Y Zhu","year":"2005","unstructured":"Zhu, Q.-Y., Qin, A.K., Suganthan, P.N., Huang, G.-B.: Evolutionary extreme learning machine. Pattern Recognit. 38(10), 1759\u20131763 (2005)","journal-title":"Pattern Recognit."},{"key":"4_CR4","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.neunet.2014.10.001","volume":"61","author":"G Huang","year":"2015","unstructured":"Huang, G., Huang, G.B., Song, S., You, K.: Trends in extreme learning machines: a review. Neural Netw. 61, 32\u201348 (2015)","journal-title":"Neural Netw."},{"issue":"3","key":"4_CR5","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1007\/s11063-012-9236-y","volume":"36","author":"J Cao","year":"2012","unstructured":"Cao, J., Lin, Z., Huang, G.B.: Self-adaptive evolutionary extreme learning machine. Neural Process. Lett. 36(3), 285\u2013305 (2012)","journal-title":"Neural Process. Lett."},{"issue":"1","key":"4_CR6","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/4235.585893","volume":"1","author":"DH Wolpert","year":"1997","unstructured":"Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67\u201382 (1997)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"4_CR7","unstructured":"Molina, D., Herrera, F.: Hibridaci\u00f3n iterativa de DE con b\u00fasqueda local con reinicio para problemas de alta dimensionalidad. In: XVI Conferencia CAEPIA, pp. 251\u2013260 (2015)"},{"issue":"1\u20133","key":"4_CR8","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"GB Huang","year":"2006","unstructured":"Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1\u20133), 489\u2013501 (2006)","journal-title":"Neurocomputing"},{"issue":"December","key":"4_CR9","first-page":"143","volume":"21","author":"H Kong","year":"2013","unstructured":"Kong, H.: Evolving extreme learning machine paradigm with adaptive operator selection and parameter control. Int. J. Uncertainty, Fuzziness Knowl.-Base Syst. 21(December), 143\u2013154 (2013)","journal-title":"Int. J. Uncertainty, Fuzziness Knowl.-Base Syst."},{"key":"4_CR10","unstructured":"Luke, S.: Essentials of Metaheuristics (2013)"},{"key":"4_CR11","unstructured":"Qin, A.K., Suganthan, P.N.: Self-adaptive differential evolution algorithm for numerical optimization. In: 2005 IEEE Congress on Evolutionary Computation, pp. 1785\u20131791 (2005)"},{"key":"4_CR12","doi-asserted-by":"publisher","first-page":"760","DOI":"10.1016\/j.advengsoft.2011.05.014","volume":"42","author":"AJ Nebro","year":"2011","unstructured":"Nebro, A.J., Durillo, J.J.: jMetal: a Java framework for multi-objective optimization. Adv. Eng. Softw. 42, 760\u2013771 (2011)","journal-title":"Adv. Eng. Softw."},{"issue":"15","key":"4_CR13","doi-asserted-by":"publisher","first-page":"2985","DOI":"10.1016\/j.ins.2008.02.017","volume":"178","author":"Z Yang","year":"2008","unstructured":"Yang, Z., Tang, K., Yao, X.: Large scale evolutionary optimization using cooperative coevolution. Inf. Sci. (Ny) 178(15), 2985\u20132999 (2008)","journal-title":"Inf. Sci. (Ny)"},{"key":"4_CR14","doi-asserted-by":"crossref","unstructured":"LaTorre, A., Muelas, S., Pe\u00f1a, J.M.: Multiple offspring sampling in large scale global optimization. In: IEEE World Congress on Computational Intelligence, WCCI 2012 (2012)","DOI":"10.1109\/CEC.2012.6256611"}],"container-title":["Lecture Notes in Computer Science","Machine Learning, Optimization, and Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-13709-0_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T14:56:11Z","timestamp":1709823371000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-13709-0_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030137083","9783030137090"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-13709-0_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"14 February 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"LOD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning, Optimization, and Data Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Volterra","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":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mod2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/lod2018.icas.xyz\/","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, in-house system","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"126","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":"46","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":"37% - 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":"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":"1.5","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)"}}]}}