{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,5]],"date-time":"2025-07-05T16:10:10Z","timestamp":1751731810195,"version":"3.41.0"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319959320"},{"type":"electronic","value":"9783319959337"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"unspecified","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":[[2018]]},"DOI":"10.1007\/978-3-319-95933-7_80","type":"book-chapter","created":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T06:48:38Z","timestamp":1530773318000},"page":"717-728","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Comparison Study of Surrogate Model Based Preselection in Evolutionary Optimization"],"prefix":"10.1007","author":[{"given":"Hao","family":"Hao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinyuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aimin","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,7,6]]},"reference":[{"key":"80_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-0663-7","volume-title":"Optimization: Algorithms and Consistent Approximations","author":"E Polak","year":"1997","unstructured":"Polak, E.: Optimization: Algorithms and Consistent Approximations. Springer, New York (1997). https:\/\/doi.org\/10.1007\/978-1-4612-0663-7"},{"issue":"C","key":"80_CR2","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1016\/j.neucom.2014.04.071","volume":"146","author":"X-F Lu","year":"2014","unstructured":"Lu, X.-F., Tang, K., Sendhoff, B., Yao, X.: A new self-adaptation scheme for differential evolution. Neurocomputing 146(C), 2\u201316 (2014)","journal-title":"Neurocomputing"},{"issue":"2","key":"80_CR3","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1504\/IJBIC.2014.060609","volume":"6","author":"R Mallipeddi","year":"2014","unstructured":"Mallipeddi, R., Suganthan, P.N.: Unit commitment - a survey and comparison of conventional and nature inspired algorithms. Int. J. Bio-Inspir. Comput. 6(2), 71\u201390 (2014)","journal-title":"Int. J. Bio-Inspir. Comput."},{"key":"80_CR4","doi-asserted-by":"crossref","unstructured":"Back, T., Schwefel, H.-P.: Evolutionary computation: an overview. In: 1996 IEEE International Congress on Evolutionary Computation (CEC), pp. 20\u201329 (1996)","DOI":"10.1109\/ICEC.1996.542329"},{"key":"80_CR5","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780195099713.001.0001","volume-title":"Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming","author":"T Back","year":"1996","unstructured":"Back, T.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming. Genetic Algorithms. Oxford University Press, New York (1996)"},{"key":"80_CR6","unstructured":"Cavicchio, D.J.: Adaptive search using simulated evolution. Unpublished doctoral dissertation, University of Michigan, Ann Arbor (1970)"},{"key":"80_CR7","unstructured":"Mahfoud, S.W.: Crowding and preselection revisited. In: Parallel Problem Solving from Nature (PPSN), pp. 27\u201336. Amsterdam Press, North-Holland (1992)"},{"key":"80_CR8","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1109\/TEVC.2010.2087271","volume":"15","author":"Y Wang","year":"2011","unstructured":"Wang, Y., Cai, Z., Zhang, Q.: Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans. Evol. Comput. 15, 55\u201366 (2011)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"80_CR9","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhou, A., Zhang, G.: An MOEA\/D with multiple differential evolution mutation operators. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 397\u2013404 (2014)","DOI":"10.1109\/CEC.2014.6900339"},{"issue":"1","key":"80_CR10","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s00500-003-0328-5","volume":"9","author":"Y Jin","year":"2003","unstructured":"Jin, Y.: A comprehensive survey of fitness approximation in evolutionary computation. Soft. Comput. 9(1), 3\u201312 (2003)","journal-title":"Soft. Comput."},{"issue":"2","key":"80_CR11","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.swevo.2011.05.001","volume":"1","author":"Y Jin","year":"2011","unstructured":"Jin, Y.: Surrogate-assisted evolutionary computation: recent advances and future challenges. Swarm Evol. Comput. 1(2), 61\u201370 (2011)","journal-title":"Swarm Evol. Comput."},{"key":"80_CR12","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zhou, A., Zhang, G.: Preselection via classification: a case study on global optimization. Int. J. Bio-Inspir. Comput. (2018, accepted)","DOI":"10.1504\/IJBIC.2018.092807"},{"key":"80_CR13","doi-asserted-by":"crossref","unstructured":"Lu, X., Tang, K., Yao, X.: Classification-assisted differential evolution for computationally expensive problems. In: 2011 IEEE Congress on Evolutionary Computation (CEC), pp. 1986\u20131993 (2011)","DOI":"10.1109\/CEC.2011.5949859"},{"key":"80_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1007\/3-540-45712-7_35","volume-title":"Parallel Problem Solving from Nature \u2014 PPSN VII","author":"M Emmerich","year":"2002","unstructured":"Emmerich, M., Giotis, A., \u00d6zdemir, M., B\u00e4ck, T., Giannakoglou, K.: Metamodel\u2014assisted evolution strategies. In: Guerv\u00f3s, J.J.M., Adamidis, P., Beyer, H.-G., Schwefel, H.-P., Fern\u00e1ndez-Villaca\u00f1as, J.-L. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 361\u2013370. Springer, Heidelberg (2002). https:\/\/doi.org\/10.1007\/3-540-45712-7_35"},{"key":"80_CR15","unstructured":"El-beltagy, M.A., Keane, A.J.: Evolutionary optimization for computationally expensive problems using Gaussian processes. In: Arabnia, H. (ed.) Proceedings of International Conference on Artificial Intelligence IC-AI\u20192001. CSREA Press (2001)"},{"issue":"2","key":"80_CR16","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/s12293-016-0199-9","volume":"10","author":"C Sun","year":"2018","unstructured":"Sun, C., Ding, J., Zeng, V., Jin, Y.: A fitness approximation assisted competitive swarm optimizer for large scale expensive optimization problems. Memetic Comput. 10(2), 123\u2013134 (2018)","journal-title":"Memetic Comput."},{"issue":"5","key":"80_CR17","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1109\/TEVC.2002.800884","volume":"6","author":"Y Jin","year":"2002","unstructured":"Jin, Y., Olhofer, M., Sendhoff, B.: A framework for evolutionary optimization with approximate fitness functions. IEEE Trans. Evol. Comput. 6(5), 481\u2013494 (2002)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"8","key":"80_CR18","doi-asserted-by":"publisher","first-page":"781","DOI":"10.1007\/s00500-008-0348-2","volume":"13","author":"Y Tenne","year":"2009","unstructured":"Tenne, Y., Armfield, S.W.: A framework for memetic optimization using variable global and local surrogate models. Soft. Comput. 13(8), 781\u2013793 (2009)","journal-title":"Soft. Comput."},{"issue":"1","key":"80_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00158-015-1226-z","volume":"52","author":"M Tabatabaei","year":"2015","unstructured":"Tabatabaei, M., Hakanen, J., Hartikainen, M., Miettinen, K., Sindhya, K.: A survey on handling computationally expensive multiobjective optimization problems using surrogates: non-nature inspired methods. Struct. Multidiscip. Opt. 52(1), 1\u201324 (2015)","journal-title":"Struct. Multidiscip. Opt."},{"issue":"3","key":"80_CR20","first-page":"273","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273\u2013297 (1995)","journal-title":"Mach. Learn."},{"issue":"3","key":"80_CR21","first-page":"17","volume":"40","author":"L Breiman","year":"1984","unstructured":"Breiman, L., Friedman, J., Olshen, R., Stone, C.J.: Classification and regression trees. Biometrics 40(3), 17\u201323 (1984)","journal-title":"Biometrics"},{"issue":"2","key":"80_CR22","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1109\/TEVC.2013.2248012","volume":"18","author":"B Liu","year":"2014","unstructured":"Liu, B., Zhang, Q., Gielen, G.G.E.: A Gaussian process surrogate model assisted evolutionary algorithm for medium scale expensive optimization problems. IEEE Trans. Evol. Comput. 18(2), 180\u2013192 (2014)","journal-title":"IEEE Trans. Evol. Comput."}],"container-title":["Lecture Notes in Computer Science","Intelligent Computing Theories and Application"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-95933-7_80","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,5]],"date-time":"2025-07-05T15:43:17Z","timestamp":1751730197000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-95933-7_80"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783319959320","9783319959337"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-95933-7_80","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"6 July 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Wuhan","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":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 August 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 August 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ic-ic.tongji.edu.cn\/2018\/index.htm","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":"LOD","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"632","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":"275","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":"72","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":"44% - 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":"3.46","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":"0","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)"}}]}}