{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T01:00:37Z","timestamp":1740099637407,"version":"3.37.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030386283"},{"type":"electronic","value":"9783030386290"}],"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"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-38629-0_30","type":"book-chapter","created":{"date-parts":[[2020,1,21]],"date-time":"2020-01-21T09:05:05Z","timestamp":1579597505000},"page":"370-382","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Optimization of Generalized Halton Sequences by Differential Evolution"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8428-3332","authenticated-orcid":false,"given":"Pavel","family":"Kr\u00f6mer","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8481-0136","authenticated-orcid":false,"given":"Jan","family":"Plato\u0161","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9600-8319","authenticated-orcid":false,"given":"V\u00e1clav","family":"Sn\u00e1\u0161el","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,1,22]]},"reference":[{"issue":"2","key":"30_CR1","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1287\/ijoc.6.2.154","volume":"6","author":"JC Bean","year":"1994","unstructured":"Bean, J.C.: Genetic algorithms and random keys for sequencing and optimization. ORSA J. Comput. 6(2), 154\u2013160 (1994). \nhttps:\/\/doi.org\/10.1287\/ijoc.6.2.154","journal-title":"ORSA J. Comput."},{"issue":"1","key":"30_CR2","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.matcom.2005.03.004","volume":"70","author":"H Chi","year":"2005","unstructured":"Chi, H., Mascagni, M., Warnock, T.: On the optimal Halton sequence. Math. Comput. Simul. 70(1), 9\u201321 (2005). \nhttps:\/\/doi.org\/10.1016\/j.matcom.2005.03.004","journal-title":"Math. Comput. Simul."},{"key":"30_CR3","doi-asserted-by":"publisher","unstructured":"De Rainville, F.M., Gagn\u00e9, C., Teytaud, O., Laurendeau, D.: Optimizing low-discrepancy sequences with an evolutionary algorithm. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, GECCO 2009, pp. 1491\u20131498. ACM, New York (2009). \nhttps:\/\/doi.org\/10.1145\/1569901.1570101","DOI":"10.1145\/1569901.1570101"},{"issue":"2","key":"30_CR4","doi-asserted-by":"publisher","first-page":"9:1","DOI":"10.1145\/2133390.2133393","volume":"22","author":"FM Rainville De","year":"2012","unstructured":"De Rainville, F.M., Gagn\u00e9, C., Teytaud, O., Laurendeau, D.: Evolutionary optimization of low-discrepancy sequences. ACM Trans. Model. Comput. Simul. 22(2), 9:1\u20139:25 (2012). \nhttps:\/\/doi.org\/10.1145\/2133390.2133393","journal-title":"ACM Trans. Model. Comput. Simul."},{"key":"30_CR5","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511761188","volume-title":"Digital Nets and Sequences: Discrepancy Theory and Quasi\u2013Monte Carlo Integration","author":"J Dick","year":"2010","unstructured":"Dick, J., Pillichshammer, F.: Digital Nets and Sequences: Discrepancy Theory and Quasi\u2013Monte Carlo Integration. Cambridge University Press, Cambridge (2010)"},{"key":"30_CR6","doi-asserted-by":"publisher","unstructured":"Doerr, C., De Rainville, F.M.: Constructing low star discrepancy point sets with genetic algorithms. In: Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013, pp. 789\u2013796. ACM, New York (2013). \nhttps:\/\/doi.org\/10.1145\/2463372.2463469","DOI":"10.1145\/2463372.2463469"},{"key":"30_CR7","series-title":"Lecture Notes in Mathematics","doi-asserted-by":"publisher","DOI":"10.1007\/BFb0093404","volume-title":"Sequences, Discrepancies and Applications","author":"M Drmota","year":"1997","unstructured":"Drmota, M., Tichy, R.F.: Sequences, Discrepancies and Applications. LNM, vol. 1651. Springer, Heidelberg (1997). \nhttps:\/\/doi.org\/10.1007\/BFb0093404"},{"key":"30_CR8","doi-asserted-by":"publisher","DOI":"10.1002\/9780470512517","volume-title":"Computational Intelligence: An Introduction","author":"A Engelbrecht","year":"2007","unstructured":"Engelbrecht, A.: Computational Intelligence: An Introduction, 2nd edn. Wiley, New York (2007)","edition":"2"},{"issue":"4","key":"30_CR9","doi-asserted-by":"publisher","first-page":"15:1","DOI":"10.1145\/1596519.1596520","volume":"19","author":"H Faure","year":"2009","unstructured":"Faure, H., Lemieux, C.: Generalized Halton sequences in 2008: a comparative study. ACM Trans. Model. Comput. Simul. 19(4), 15:1\u201315:31 (2009). \nhttps:\/\/doi.org\/10.1145\/1596519.1596520","journal-title":"ACM Trans. Model. Comput. Simul."},{"issue":"2","key":"30_CR10","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1007\/s10479-016-2331-0","volume":"265","author":"P Kr\u00f6mer","year":"2018","unstructured":"Kr\u00f6mer, P., Plato\u0161, J., Nowakov\u00e1, J., Sn\u00e1\u0161el, V.: Optimal column subset selection for image classification by genetic algorithms. Ann. Oper. Res. 265(2), 205\u2013222 (2018). \nhttps:\/\/doi.org\/10.1007\/s10479-016-2331-0","journal-title":"Ann. Oper. Res."},{"key":"30_CR11","doi-asserted-by":"publisher","unstructured":"Kr\u00f6mer, P., Platos, J., Sn\u00e1sel, V.: Traditional and self-adaptive differential evolution for the p-median problem. In: 2nd IEEE International Conference on Cybernetics, CYBCONF 2015, Gdynia, Poland, 24\u201326 June 2015, pp. 299\u2013304 (2015). \nhttps:\/\/doi.org\/10.1109\/CYBConf.2015.7175950","DOI":"10.1109\/CYBConf.2015.7175950"},{"key":"30_CR12","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.advengsoft.2012.09.003","volume":"55","author":"X Li","year":"2013","unstructured":"Li, X., Yin, M.: An opposition-based differential evolution algorithm for permutation flow shop scheduling based on diversity measure. Adv. Eng. Softw. 55, 10\u201331 (2013). \nhttps:\/\/doi.org\/10.1016\/j.advengsoft.2012.09.003","journal-title":"Adv. Eng. Softw."},{"key":"30_CR13","doi-asserted-by":"publisher","unstructured":"Ponsich, A., Tapia, M.G.C., Coello, C.A.C.: Solving permutation problems with differential evolution: an application to the jobshop scheduling problem. In: 2009 Ninth International Conference on Intelligent Systems Design and Applications, pp. 25\u201330, November 2009. \nhttps:\/\/doi.org\/10.1109\/ISDA.2009.49","DOI":"10.1109\/ISDA.2009.49"},{"key":"30_CR14","series-title":"Natural Computing Series","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-31306-0","volume-title":"Differential Evolution: A Practical Approach to Global Optimization","author":"KV Price","year":"2005","unstructured":"Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. NCS. Springer, Heidelberg (2005). \nhttps:\/\/doi.org\/10.1007\/3-540-31306-0"},{"issue":"7","key":"30_CR15","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1007\/s00170-007-1115-8","volume":"38","author":"B Qian","year":"2008","unstructured":"Qian, B., Wang, L., Hu, R., Wang, W.L., Huang, D.X., Wang, X.: A hybrid differential evolution method for permutation flow-shop scheduling. Int. J. Adv. Manuf. Technol. 38(7), 757\u2013777 (2008). \nhttps:\/\/doi.org\/10.1007\/s00170-007-1115-8","journal-title":"Int. J. Adv. Manuf. Technol."},{"issue":"1","key":"30_CR16","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.ejor.2004.09.057","volume":"174","author":"LV Snyder","year":"2006","unstructured":"Snyder, L.V., Daskin, M.S.: A random-key genetic algorithm for the generalized traveling salesman problem. Eur. J. Oper. Res. 174(1), 38\u201353 (2006)","journal-title":"Eur. J. Oper. Res."},{"issue":"3","key":"30_CR17","doi-asserted-by":"publisher","first-page":"1930","DOI":"10.1016\/j.ejor.2005.12.024","volume":"177","author":"MF Tasgetiren","year":"2007","unstructured":"Tasgetiren, M.F., Liang, Y.C., Sevkli, M., Gencyilmaz, G.: A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem. Eur. J. Oper. Res. 177(3), 1930\u20131947 (2007). \nhttps:\/\/doi.org\/10.1016\/j.ejor.2005.12.024","journal-title":"Eur. J. Oper. Res."},{"issue":"1","key":"30_CR18","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/j.cam.2005.05.022","volume":"189","author":"B Vandewoestyne","year":"2006","unstructured":"Vandewoestyne, B., Cools, R.: Good permutations for deterministic scrambled Halton sequences in terms of l2-discrepancy. J. Comput. Appl. Math. 189(1), 341\u2013361 (2006). \nhttps:\/\/doi.org\/10.1016\/j.cam.2005.05.022","journal-title":"J. Comput. Appl. Math."}],"container-title":["Lecture Notes in Computer Science","Learning and Intelligent Optimization"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-38629-0_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,1,21]],"date-time":"2020-01-21T09:21:46Z","timestamp":1579598506000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-38629-0_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030386283","9783030386290"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-38629-0_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"22 January 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"LION","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Learning and Intelligent Optimization","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chania, Crete","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 May 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 May 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"lion2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.lion13.pem.tuc.gr\/en\/home\/","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":"52","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":"38","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":"73% - 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","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)"}}]}}