{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T07:02:15Z","timestamp":1769583735068,"version":"3.49.0"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030581145","type":"print"},{"value":"9783030581152","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","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":[[2020]]},"DOI":"10.1007\/978-3-030-58115-2_13","type":"book-chapter","created":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T22:02:51Z","timestamp":1598997771000},"page":"186-200","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Multi-objective Optimization by Uncrowded Hypervolume Gradient Ascent"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0057-1535","authenticated-orcid":false,"given":"Timo M.","family":"Deist","sequence":"first","affiliation":[]},{"given":"Stefanus C.","family":"Maree","sequence":"additional","affiliation":[]},{"given":"Tanja","family":"Alderliesten","sequence":"additional","affiliation":[]},{"given":"Peter A. N.","family":"Bosman","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,2]]},"reference":[{"key":"13_CR1","doi-asserted-by":"crossref","unstructured":"Auger, A., Bader, J., Brockhoff, D., Zitzler, E.: Theory of the hypervolume indicator: Optimal $$\\mu $$-distributions and the choice of the reference point. In: Proceedings of the Tenth ACM SIGEVO Workshop on Foundations of Genetic Algorithms - FOGA 2009, pp. 87\u2013102. ACM Press, New York (2009)","DOI":"10.1145\/1527125.1527138"},{"issue":"3","key":"13_CR2","doi-asserted-by":"publisher","first-page":"1653","DOI":"10.1016\/j.ejor.2006.08.008","volume":"181","author":"N Beume","year":"2007","unstructured":"Beume, N., Naujoks, B., Emmerich, M.: SMS-EMOA: multiobjective selection based on dominated hypervolume. Eur. J. Oper. Res. 181(3), 1653\u20131669 (2007)","journal-title":"Eur. J. Oper. Res."},{"issue":"2","key":"13_CR3","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1109\/TEVC.2003.810761","volume":"7","author":"PAN Bosman","year":"2003","unstructured":"Bosman, P.A.N., Thierens, D.: The balance between proximity and diversity in multiobjective evolutionary algorithms. IEEE Trans. Evol. Comput. 7(2), 174\u2013188 (2003)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"1","key":"13_CR4","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1109\/TEVC.2010.2051445","volume":"16","author":"PAN Bosman","year":"2011","unstructured":"Bosman, P.A.N.: On gradients and hybrid evolutionary algorithms for real-valued multiobjective optimization. IEEE Trans. Evol. Comput. 16(1), 51\u201369 (2011)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"13_CR5","volume-title":"Multi-Objective Optimization","author":"K Deb","year":"2001","unstructured":"Deb, K.: Multi-Objective Optimization. Wiley, Chichester (2001)"},{"issue":"5\u20136","key":"13_CR6","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1016\/j.crma.2012.03.014","volume":"350","author":"JA D\u00e9sid\u00e9ri","year":"2012","unstructured":"D\u00e9sid\u00e9ri, J.A.: Multiple-gradient descent algorithm (MGDA) for multiobjective optimization. C.R. Math. 350(5\u20136), 313\u2013318 (2012)","journal-title":"C.R. Math."},{"issue":"3","key":"13_CR7","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1007\/s11047-018-9685-y","volume":"17","author":"MTM Emmerich","year":"2018","unstructured":"Emmerich, M.T.M., Deutz, A.H.: A tutorial on multiobjective optimization: fundamentals and evolutionary methods. Nat. Comput. 17(3), 585\u2013609 (2018). https:\/\/doi.org\/10.1007\/s11047-018-9685-y","journal-title":"Nat. Comput."},{"key":"13_CR8","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/978-3-319-01460-9_8","volume-title":"EVOLVE-A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation III","author":"M Emmerich","year":"2014","unstructured":"Emmerich, M., Deutz, A.: Time complexity and zeros of the hypervolume indicator gradient field. In: Schuetze, O., et al. (eds.) EVOLVE-A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation III, pp. 169\u2013193. Springer, Heidelberg (2014). https:\/\/doi.org\/10.1007\/978-3-319-01460-9_8"},{"key":"13_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1007\/3-540-36970-8_37","volume-title":"Evolutionary Multi-Criterion Optimization","author":"M Fleischer","year":"2003","unstructured":"Fleischer, M.: The measure of pareto optima applications to multi-objective etaheuristics. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Thiele, L., Deb, K. (eds.) EMO 2003. LNCS, vol. 2632, pp. 519\u2013533. Springer, Heidelberg (2003). https:\/\/doi.org\/10.1007\/3-540-36970-8_37"},{"issue":"3","key":"13_CR10","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1007\/s001860000043","volume":"51","author":"J Fliege","year":"2000","unstructured":"Fliege, J., Svaiter, B.F.: Steepest descent methods for multicriteria optimization. Math. Methods Oper. Res. 51(3), 479\u2013494 (2000)","journal-title":"Math. Methods Oper. Res."},{"key":"13_CR11","unstructured":"Fonseca, C.M., Paquete, L., L\u00f3pez-Ib\u00e1nez, M.: An improved dimension-sweep algorithm for the hypervolume indicator. In: 2006 IEEE International Conference on Evolutionary Computation, pp. 1157\u20131163. IEEE (2006)"},{"key":"13_CR12","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/978-3-319-07494-8_2","volume-title":"EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V","author":"VA Sosa Hern\u00e1ndez","year":"2014","unstructured":"Sosa Hern\u00e1ndez, V.A., Sch\u00fctze, O., Emmerich, M.: Hypervolume maximization via set based Newton\u2019s method. In: Tantar, A.A., et al. (eds.) EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V. AISC, vol. 288, pp. 15\u201328. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-07494-8_2"},{"key":"13_CR13","unstructured":"Huband, S., Barone, L., While, L., Hingston, P.: Walking fish group toolkit: C++ source code. http:\/\/www.wfg.csse.uwa.edu.au\/toolkit\/ . Accessed 06 Apr 2020"},{"key":"13_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1007\/978-3-540-31880-4_20","volume-title":"Evolutionary Multi-Criterion Optimization","author":"S Huband","year":"2005","unstructured":"Huband, S., Barone, L., While, L., Hingston, P.: A scalable multi-objective test problem toolkit. In: Coello Coello, C.A., Hern\u00e1ndez Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 280\u2013295. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/978-3-540-31880-4_20"},{"issue":"5","key":"13_CR15","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1109\/TEVC.2005.861417","volume":"10","author":"S Huband","year":"2006","unstructured":"Huband, S., Hingston, P., Barone, L., While, L.: A review of multiobjective test problems and a scalable test problem toolkit. IEEE Trans. Evol. Comput. 10(5), 477\u2013506 (2006)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"13_CR16","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"13_CR17","doi-asserted-by":"crossref","unstructured":"Kuhn, H.W., Tucker, A.W.: Nonlinear programming. In: Proceedings of the 2nd Berkeley Symposium on Mathematical and Statistical Probability, pp. 481\u2013492. University of California Press (1951)","DOI":"10.1525\/9780520411586-036"},{"issue":"2","key":"13_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3300148","volume":"52","author":"M Li","year":"2019","unstructured":"Li, M., Yao, X.: Quality evaluation of solution sets in multiobjective optimisation: a survey. ACM Comput. Surv. (CSUR) 52(2), 1\u201338 (2019)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"13_CR19","unstructured":"Maree, S.C., Alderliesten, T., Bosman, P.A.N.: Uncrowded hypervolume-based multi-objective optimization with gene-pool optimal mixing. arXiv preprint arXiv:2004.05068 (2020)"},{"key":"13_CR20","doi-asserted-by":"crossref","unstructured":"Peitz, S., Dellnitz, M.: Gradient-based multiobjective optimization with uncertainties. arXiv preprint arXiv:1612.03815v2 (2017)","DOI":"10.1007\/978-3-319-64063-1_7"},{"issue":"1","key":"13_CR21","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1023\/A:1015472306888","volume":"114","author":"S Sch\u00e4ffler","year":"2002","unstructured":"Sch\u00e4ffler, S., Schultz, R., Weinzierl, K.: Stochastic method for the solution of unconstrained vector optimization problems. J. Optim. Theory Appl. 114(1), 209\u2013222 (2002)","journal-title":"J. Optim. Theory Appl."},{"issue":"3","key":"13_CR22","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/s10732-016-9310-0","volume":"22","author":"O Sch\u00fctze","year":"2016","unstructured":"Sch\u00fctze, O., Hern\u00e1ndez, V.A.S., Trautmann, H., Rudolph, G.: The hypervolume based directed search method for multi-objective optimization problems. J. Heuristics 22(3), 273\u2013300 (2016). https:\/\/doi.org\/10.1007\/s10732-016-9310-0","journal-title":"J. Heuristics"},{"issue":"2","key":"13_CR23","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1007\/s10589-015-9774-0","volume":"63","author":"O Sch\u00fctze","year":"2015","unstructured":"Sch\u00fctze, O., Mart\u00edn, A., Lara, A., Alvarado, S., Salinas, E., Coello, C.A.C.: The directed search method for multi-objective memetic algorithms. Comput. Optim. Appl. 63(2), 305\u2013332 (2015). https:\/\/doi.org\/10.1007\/s10589-015-9774-0","journal-title":"Comput. Optim. Appl."},{"key":"13_CR24","doi-asserted-by":"crossref","unstructured":"Tour\u00e9, C., Hansen, N., Auger, A., Brockhoff, D.: Uncrowded hypervolume improvement: COMO-CMA-ES and the sofomore framework. In: Proceedings of the Genetic and Evolutionary Computation Conference, New York, NY, USA, pp. 638\u2013646 (2019)","DOI":"10.1145\/3321707.3321852"},{"key":"13_CR25","unstructured":"Wang, H.: Hypervolume indicator gradient ascent multi-objective optimization. https:\/\/github.com\/wangronin\/HIGA-MO . Accessed 11 April 2020"},{"key":"13_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1007\/978-3-319-54157-0_44","volume-title":"Evolutionary Multi-Criterion Optimization","author":"H Wang","year":"2017","unstructured":"Wang, H., Deutz, A., B\u00e4ck, T., Emmerich, M.: Hypervolume indicator gradient ascent multi-objective optimization. In: Trautmann, H., et al. (eds.) EMO 2017. LNCS, vol. 10173, pp. 654\u2013669. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-54157-0_44"},{"key":"13_CR27","series-title":"Studies in Computational Intelligence","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1007\/978-3-319-44003-3_8","volume-title":"NEO 2015","author":"H Wang","year":"2017","unstructured":"Wang, H., Ren, Y., Deutz, A., Emmerich, M.: On steering dominated points in hypervolume indicator gradient ascent for bi-objective optimization. In: Sch\u00fctze, O., Trujillo, L., Legrand, P., Maldonado, Y. (eds.) NEO 2015. SCI, vol. 663, pp. 175\u2013203. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-44003-3_8"},{"key":"13_CR28","unstructured":"Wessing, S.: Optproblems: Infrastructure to define optimization problems and some test problems for black-box optimization. Python package version 1.2 (2018)"},{"issue":"2","key":"13_CR29","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1109\/TEVC.2003.810758","volume":"7","author":"E Zitzler","year":"2003","unstructured":"Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C., Da Fonseca, V.: Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans. Evol. Comput. 7(2), 117\u2013132 (2003)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"13_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1007\/BFb0056872","volume-title":"Parallel Problem Solving from Nature \u2013 PPSN V. PPSN 1998","author":"E Zitzler","year":"1998","unstructured":"Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms \u2014 a case study. In: Eiben, A.E., B\u00e4ck, T., Schoenauer, M., Schwefel, H.P. (eds.) Parallel Problem Solving from Nature \u2013 PPSN V. PPSN 1998. Lecture Notes in Computer Science, vol. 1498, pp. 292\u2013301. Springer, Heidelberg (1998). https:\/\/doi.org\/10.1007\/BFb0056872"}],"container-title":["Lecture Notes in Computer Science","Parallel Problem Solving from Nature \u2013 PPSN XVI"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58115-2_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T00:45:14Z","timestamp":1723509914000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-58115-2_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030581145","9783030581152"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58115-2_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"2 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PPSN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Parallel Problem Solving from Nature","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Leiden","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The Netherlands","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ppsn2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ppsn2020.liacs.leidenuniv.nl\/","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":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"268","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":"99","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":"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":"2.2","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)"}}]}}