{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T12:43:12Z","timestamp":1743079392923,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030438869"},{"type":"electronic","value":"9783030438876"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-43887-6_45","type":"book-chapter","created":{"date-parts":[[2020,3,27]],"date-time":"2020-03-27T15:03:32Z","timestamp":1585321412000},"page":"509-516","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["OPTISIA: An Evolutionary Approach to Parameter Optimisation in a Family of Point-Set Pattern-Discovery Algorithms"],"prefix":"10.1007","author":[{"given":"Viktor","family":"Schmuck","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Meredith","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,3,28]]},"reference":[{"issue":"3","key":"45_CR1","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1080\/09298215.2016.1208666","volume":"45","author":"P Boot","year":"2016","unstructured":"Boot, P., Volk, A., de Haas, W.B.: Evaluating the role of repeated patterns in folk song classification and compression. J. New Music Res. 45(3), 223\u2013238 (2016)","journal-title":"J. New Music Res."},{"issue":"4","key":"45_CR2","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1162\/0148926042728449","volume":"28","author":"R Cilibrasi","year":"2004","unstructured":"Cilibrasi, R., Vit\u00e1nyi, P.M.B., de Wolf, R.: Algorithmic clustering of music based on string compression. Comput. Music J. 28(4), 49\u201367 (2004)","journal-title":"Comput. Music J."},{"key":"45_CR3","unstructured":"Collins, T.: Improved methods for pattern discovery in music, with applications in automated stylistic composition. Ph.D. thesis, Faculty of Mathematics, Computing and Technology, The Open University, Milton Keynes (2011)"},{"key":"45_CR4","unstructured":"Collins, T.: JKU Patterns Development Database (2013). https:\/\/dl.dropbox.com\/u\/11997856\/JKU\/JKUPDD-Aug2013.zip"},{"key":"45_CR5","unstructured":"Collins, T., Thurlow, J., Laney, R., Willis, A., Garthwaite, P.H.: A comparative evaluation of algorithms for discovering translational patterns in Baroque keyboard works. In: 11th International Society for Music Information Retrieval Conference (ISMIR 2010), pp. 3\u20138 (2010)"},{"key":"45_CR6","unstructured":"Forth, J.C.: Cognitively-motivated geometric methods of pattern discovery and models of similarity in music. Ph.D. thesis, Department of Computing, Goldsmiths, University of London (2012)"},{"key":"45_CR7","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.is.2018.01.003","volume":"74","author":"CD Francescomarino","year":"2018","unstructured":"Francescomarino, C.D., et al.: Genetic algorithms for hyperparameter optimization in predictive business process monitoring. Inf. Syst. 74, 67\u201383 (2018)","journal-title":"Inf. Syst."},{"key":"45_CR8","unstructured":"Giraud, M., Groult, R., Lev\u00e9, F.: Truth file for the analysis of Bach and Shostakovich fugues (2013\/12\/27 version) (2013). http:\/\/www.algomus.fr\/truth\/fugues.truth.2013.12"},{"key":"45_CR9","first-page":"165","volume-title":"Leading Edge Computer Science Research","author":"MW Gutowski","year":"2005","unstructured":"Gutowski, M.W.: Biology, physics, small worlds and genetic algorithms. In: Shannon, S. (ed.) Leading Edge Computer Science Research, pp. 165\u2013218. Nova Science Publishers Inc., New York (2005)"},{"key":"45_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1007\/3-540-58483-8_7","volume-title":"Evolutionary Computing","author":"PJB Hancock","year":"1994","unstructured":"Hancock, P.J.B.: An empirical comparison of selection methods in evolutionary algorithms. In: Fogarty, T.C. (ed.) AISB EC 1994. LNCS, vol. 865, pp. 80\u201394. Springer, Heidelberg (1994). https:\/\/doi.org\/10.1007\/3-540-58483-8_7"},{"key":"45_CR11","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2017.2737984","author":"D Herremans","year":"2017","unstructured":"Herremans, D., Chew, E.: MorpheuS: generating structured music with constrained patterns and tension. IEEE Trans. Affect. Comput. (2017). https:\/\/doi.org\/10.1109\/TAFFC.2017.2737984","journal-title":"IEEE Trans. Affect. Comput."},{"key":"45_CR12","unstructured":"Hillewaere, R., Manderick, B., Conklin, D.: String quartet classification with monophonic models. In: 11th International Society for Music Information Retrieval Conference (ISMIR 2010), Utrecht, The Netherlands, pp. 537\u2013542 (2010)"},{"key":"45_CR13","unstructured":"Lipowski, A., Lipowska, D.: Roulette-wheel selection via stochastic acceptance. CoRR abs\/1109.3627 (2011). http:\/\/arxiv.org\/abs\/1109.3627"},{"issue":"11","key":"45_CR14","doi-asserted-by":"publisher","first-page":"796","DOI":"10.1007\/s001700050134","volume":"15","author":"Y Liu","year":"1999","unstructured":"Liu, Y., Wang, C.: A modified genetic algorithm based optimisation of milling parameters. Int. J. Adv. Manuf. Technol. 15(11), 796\u2013799 (1999)","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"45_CR15","unstructured":"Meredith, D.: OMNISIA, software. http:\/\/www.titanmusic.com\/software\/omnisia\/OMNISIA20160926.zip"},{"issue":"2","key":"45_CR16","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1080\/09298210600834961","volume":"35","author":"D Meredith","year":"2006","unstructured":"Meredith, D.: The ps13 pitch spelling algorithm. J. New Music Res. 35(2), 121\u2013159 (2006)","journal-title":"J. New Music Res."},{"key":"45_CR17","unstructured":"Meredith, D.: Computing pitch names in tonal music: a comparative analysis of pitch spelling algorithms. Ph.D. thesis, Faculty of Music, University of Oxford (2007)"},{"key":"45_CR18","unstructured":"Meredith, D.: COSIATEC and SIATECCompress: pattern discovery by geometric compression. In: MIREX 2013, Competition on Discovery of Repeated Themes & Sections (2013). https:\/\/www.music-ir.org\/mirex\/abstracts\/2013\/DM10.pdf"},{"issue":"3","key":"45_CR19","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1080\/09298215.2015.1045003","volume":"44","author":"D Meredith","year":"2015","unstructured":"Meredith, D.: Music analysis and point-set compression. J. New Music Res. 44(3), 245\u2013270 (2015)","journal-title":"J. New Music Res."},{"issue":"4","key":"45_CR20","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1076\/jnmr.31.4.321.14162","volume":"31","author":"D Meredith","year":"2002","unstructured":"Meredith, D., Lemstr\u00f6m, K., Wiggins, G.A.: Algorithms for discovering repeated patterns in multidimensional representations of polyphonic music. J. New Music Res. 31(4), 321\u2013345 (2002)","journal-title":"J. New Music Res."},{"key":"45_CR21","unstructured":"Meredith, D., Lemstr\u00f6m, K., Wiggins, G.A.: Algorithms for discovering repeated patterns in multidimensional representations of polyphonic music. In: Cambridge Music Processing Colloquium (2003). http:\/\/www.titanmusic.com\/papers\/public\/cmpc2003.pdf"},{"issue":"5","key":"45_CR22","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1016\/0005-1098(78)90005-5","volume":"14","author":"J Rissanen","year":"1978","unstructured":"Rissanen, J.: Modeling by shortest data description. Automatica 14(5), 465\u2013471 (1978)","journal-title":"Automatica"},{"issue":"1","key":"45_CR23","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1049\/iet-gtd.2008.0584","volume":"4","author":"LM Saini","year":"2010","unstructured":"Saini, L.M., Aggarwal, S.K., Kumar, A.: Parameter optimisation using genetic algorithm for support vector machine-based price-forecasting model in national electricity market. IET Gener. Transm. Distrib. 4(1), 36\u201349 (2010)","journal-title":"IET Gener. Transm. Distrib."},{"issue":"12","key":"45_CR24","doi-asserted-by":"publisher","first-page":"3265","DOI":"10.1109\/TIT.2004.838346","volume":"50","author":"NK Vereshchagin","year":"2004","unstructured":"Vereshchagin, N.K., Vit\u00e1nyi, P.M.B.: Kolmogorov\u2019s structure functions and model selection. IEEE Trans. Inf. Theory 50(12), 3265\u20133290 (2004)","journal-title":"IEEE Trans. Inf. Theory"}],"container-title":["Communications in Computer and Information Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-43887-6_45","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T01:09:44Z","timestamp":1707786584000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-43887-6_45"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030438869","9783030438876"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-43887-6_45","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"28 March 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"W\u00fcrzburg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","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":"16 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ecmlpkdd2019.org\/","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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"733","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":"130","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":"18% - 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":"3.04","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":"5.3","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"ECML PKDD Workshops Information: single-blind review, submissions: 200, full papers accepted: 70, short papers accepted: 46","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}