{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T23:51:41Z","timestamp":1743033101505,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789811941085"},{"type":"electronic","value":"9789811941092"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-981-19-4109-2_33","type":"book-chapter","created":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T14:38:26Z","timestamp":1659364706000},"page":"360-368","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Uncertainty Estimation Method of Support Vector Machine Surrogate Model Assisting for Expensive Optimization"],"prefix":"10.1007","author":[{"given":"Qing","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Hanhua","family":"Zou","sequence":"additional","affiliation":[]},{"given":"Zhigao","family":"Zeng","sequence":"additional","affiliation":[]},{"given":"Sanyou","family":"Zeng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,1]]},"reference":[{"key":"33_CR1","doi-asserted-by":"crossref","unstructured":"Eiben, A.E., Jim, S.: From evolutionary computation to the evolution of things. Nature 521(7553), 476\u2013482 (2015)","DOI":"10.1038\/nature14544"},{"key":"33_CR2","unstructured":"Michael, E.: Single and multi-objective evolutionary design optimization assisted by gaussian random field metamodels. Ph.D. dissertation, LS11, FB Informatik, Universitat Dortmund, Germany (2005)"},{"key":"33_CR3","doi-asserted-by":"crossref","unstructured":"Liu, B., Aliakbarian, H., Ma, Z., Vandenbosch, G.A., Gielen, G., Excell, P.: An efficient method for antenna design optimization based on evolutionary computation and machine learning techniques. IEEE Trans. Antennas Propag. 62(1), 7\u201318 (2014)","DOI":"10.1109\/TAP.2013.2283605"},{"key":"33_CR4","doi-asserted-by":"crossref","unstructured":"Ong, Y.-S., Nair, P.B., Keane, A.J.: Evolutionary optimization of computationally expensive problems via surrogate modelling. AIAA J. 41(4), 687\u2013696 (2003)","DOI":"10.2514\/2.1999"},{"key":"33_CR5","doi-asserted-by":"crossref","unstructured":"Jin, Y., Bernhard, S.: A systems approach to evolutionary multiobjective structural optimization and beyond. IEEE Comput. Intell. Mag. 4(3), 62\u201376 (2009)","DOI":"10.1109\/MCI.2009.933094"},{"key":"33_CR6","doi-asserted-by":"crossref","unstructured":"Jin, Y., Wang, H., Chugh, T., Guo, D., Miettinen, K.: Data-driven evolutionary optimization: an overview and case studies. IEEE Trans. Evol. Comput. 23(3), 442\u2013458 (2018)","DOI":"10.1109\/TEVC.2018.2869001"},{"key":"33_CR7","unstructured":"Jin, Y., Markus, O., Bernhard, S.: On evolutionary optimization with approximate fitness functions. In: 2000 Genetic and Evolutionary Computation Conference, pp. 786\u2013793 (2000)"},{"issue":"9","key":"33_CR8","doi-asserted-by":"publisher","first-page":"2664","DOI":"10.1109\/TCYB.2017.2710978","volume":"47","author":"H Wang","year":"2017","unstructured":"Wang, H., Jin, Y., Doherty, J.: Committee-based active learning for surrogate-assisted particle swarm optimization of expensive problems. IEEE Trans. Cybern. 47(9), 2664\u20132677 (2017)","journal-title":"IEEE Trans. Cybern."},{"key":"33_CR9","doi-asserted-by":"crossref","unstructured":"Hirotaka, N., Masao, A., Koji, W.: Using support vector machines in optimization for black-box objective functions. In: Proceedings of the International Joint Conference on Neural Networks, vol. 2, pp. 1617\u20131622 (2003)","DOI":"10.1109\/IJCNN.2003.1223941"},{"issue":"20","key":"33_CR10","doi-asserted-by":"publisher","first-page":"3813","DOI":"10.1016\/j.ins.2008.05.016","volume":"178","author":"B Gerard","year":"2008","unstructured":"Gerard, B., Fabien, L., Guillaume, C., Yann, C.: Support vector regression from simulation data and few experimental samples. Inf. Sci. 178(20), 3813\u20133827 (2008)","journal-title":"Inf. Sci."},{"issue":"2","key":"33_CR11","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1007\/s00158-011-0745-5","volume":"46","author":"A Basudhar","year":"2012","unstructured":"Basudhar, A., Dribusch, C., Lacaze, S., et al.: Constrained efficient global optimization with support vector machines. Struct. Multidiscip. Optim. 46(2), 201\u2013221 (2012)","journal-title":"Struct. Multidiscip. Optim."},{"key":"33_CR12","doi-asserted-by":"crossref","unstructured":"Jurgen, B., Christian, S.: Faster convergence by means of fitness estimation. Soft Comput, 9(1), 13\u201320 (2005)","DOI":"10.1007\/s00500-003-0329-4"},{"key":"33_CR13","doi-asserted-by":"crossref","unstructured":"Jin, Y.: Surrogate-assisted evolutionary computation: recent advances and future challenges.Swarm Evol. Comput, 1(2), 61\u201370 (2011)","DOI":"10.1016\/j.swevo.2011.05.001"},{"key":"33_CR14","doi-asserted-by":"crossref","unstructured":"Jones, D.R., Schonlau, M., Welch, W.J.: Efficient global optimization of expensive black-box functions. J. Glob. Optim. 13(4), 455\u2013492 (1998)","DOI":"10.1023\/A:1008306431147"},{"key":"33_CR15","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.ins.2018.09.003","volume":"471","author":"R Jiao","year":"2019","unstructured":"Jiao, R., Zeng, S., Li, C., et al.: A complete expected improvement criterion for Gaussian process assisted highly constrained expensive optimization. Inf. Sci. 471, 80\u201396 (2019)","journal-title":"Inf. Sci."},{"issue":"02","key":"33_CR16","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1142\/S0129065704001899","volume":"14","author":"S Matthias","year":"2004","unstructured":"Matthias, S.: Gaussian processes for machine learning. Int. J. Neural Syst. 14(02), 69\u2013106 (2004)","journal-title":"Int. J. Neural Syst."},{"issue":"3","key":"33_CR17","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1109\/TEVC.2009.2033671","volume":"14","author":"Q Zhang","year":"2009","unstructured":"Zhang, Q., Liu, W., Tsang, E., et al.: Expensive multiobjective optimization by MOEA\/D with Gaussian process model. IEEE Trans. Evol. Comput. 14(3), 456\u2013474 (2009)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"4","key":"33_CR18","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1023\/A:1008202821328","volume":"11","author":"S Rainer","year":"1997","unstructured":"Rainer, S., Kenneth, P.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341\u2013359 (1997)","journal-title":"J. Glob. Optim."},{"key":"33_CR19","doi-asserted-by":"crossref","unstructured":"Gubner, J.A.: Probability and Random Processes for Electrical and Computer Engineers. Cambridge University Press, Cambridge (2006)","DOI":"10.1017\/CBO9780511813610"},{"issue":"2","key":"33_CR20","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1080\/00401706.1987.10488205","volume":"29","author":"S Michael","year":"1987","unstructured":"Michael, S.: Large sample properties of simulations using Latin hyper-cube sampling. Technometrics 29(2), 143\u2013151 (1987)","journal-title":"Technometrics"},{"key":"33_CR21","doi-asserted-by":"crossref","unstructured":"Liu, B., Zhang, Q., Gielen, G.G.: A Gaussian process surrogate model assisted evolutionary algorithm for medium scale expensive optimization problems. IEEE Trans. Evol. Comput. 18(2), 180\u2013192 (2014)","DOI":"10.1109\/TEVC.2013.2248012"},{"key":"33_CR22","unstructured":"Fabian, P., Gael, V., Alexandre, G., Vincent, M.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)"},{"key":"33_CR23","unstructured":"Liu, B., Qin, C., Zhang, Q., Liang, J., Suganthan, P.N., Qu, B.: Problem definitions and evaluation criteria for computational expensive optimization. In: IEEE Congress on Evolutionary Computation, vol. 3, pp. 2081\u20132088 (2014)"}],"container-title":["Communications in Computer and Information Science","Exploration of Novel Intelligent Optimization Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-19-4109-2_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T12:01:34Z","timestamp":1727697694000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-19-4109-2_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811941085","9789811941092"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-19-4109-2_33","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"1 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISICA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Intelligence Computation and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Giangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"20 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isica2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/gdstinfo.scau.edu.cn\/isica2021\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"99","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":"48","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":"48% - 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)"}}]}}