{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:04:34Z","timestamp":1760709874791,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030166663"},{"type":"electronic","value":"9783030166670"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-16667-0_15","type":"book-chapter","created":{"date-parts":[[2019,4,9]],"date-time":"2019-04-09T23:44:24Z","timestamp":1554853464000},"page":"217-233","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Automatic Jazz Melody Composition Through a Learning-Based Genetic Algorithm"],"prefix":"10.1007","author":[{"given":"Yong-Wook","family":"Nam","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0492-0889","authenticated-orcid":false,"given":"Yong-Hyuk","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"doi-asserted-by":"crossref","unstructured":"Montag, C., Reuter, M., Axmacher, N.: How one\u2019s favorite song activates the reward circuitry of the brain: personality matters! Behav. Brain Res. 225(2), 511\u2013514 (2011)","key":"15_CR1","DOI":"10.1016\/j.bbr.2011.08.012"},{"issue":"3","key":"15_CR2","doi-asserted-by":"publisher","first-page":"1163","DOI":"10.1353\/sof.2001.0017","volume":"79","author":"K van Eijck","year":"2001","unstructured":"van Eijck, K.: Social differentiation in musical taste patterns. Soc. Forces 79(3), 1163\u20131185 (2001)","journal-title":"Soc. Forces"},{"unstructured":"Papadopoulos, G., Wiggins, G.: AI methods for algorithmic composition: a survey, a critical view and future prospects. In: AISB Symposium on Musical Creativity, vol. 124, pp. 110\u2013117 (1999)","key":"15_CR3"},{"unstructured":"Biles, J.A.: Genjam: a genetic algorithm for generating jazz solos. In: Proceedings of the International Computer Music Association, vol. 94, pp. 131\u2013137 (1994)","key":"15_CR4"},{"issue":"1","key":"15_CR5","doi-asserted-by":"publisher","first-page":"157","DOI":"10.2298\/YJOR1001157M","volume":"20","author":"D Mati\u0107","year":"2010","unstructured":"Mati\u0107, D.: A genetic algorithm for composing music. Yugoslav J. Oper. Res. 20(1), 157\u2013177 (2010)","journal-title":"Yugoslav J. Oper. Res."},{"issue":"61801","key":"15_CR6","first-page":"437","volume":"51","author":"A Horner","year":"1991","unstructured":"Horner, A., Goldberg, D.E.: Genetic algorithms and computer-assisted music composition. Urbana 51(61801), 437\u2013441 (1991)","journal-title":"Urbana"},{"unstructured":"Quick, D.: Kulitta: A Framework for Automated Music Composition. Yale University (2014)","key":"15_CR7"},{"unstructured":"Keller, R.M., Morrison, D.R.: A grammatical approach to automatic improvisation. In: Proceedings of the Fourth Sound and Music Conference (2007)","key":"15_CR8"},{"unstructured":"Bickerman, G., Bosley, S., Swire, P., Keller, R.M.: Learning to create jazz melodies using deep belief nets. In: Proceedings of the International Conference on Computational Creativity, pp. 228\u2013237 (2010)","key":"15_CR9"},{"unstructured":"Johnson, D.D., Keller, R.M., Weintraut, N.: Learning to create jazz melodies using a product of experts. In: Proceedings of the International Conference on Computational Creativity (2017)","key":"15_CR10"},{"doi-asserted-by":"crossref","unstructured":"Fern\u00e1ndez de Vega, F.: Revisiting the 4-part harmonization problem with GAs: a critical review and proposals for improving. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1271\u20131278 (2017)","key":"15_CR11","DOI":"10.1109\/CEC.2017.7969451"},{"doi-asserted-by":"crossref","unstructured":"Nam, Y.-W., Kim, Y.-H.: A geometric evolutionary search for melody composition. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 53\u201354 (2018)","key":"15_CR12","DOI":"10.1145\/3205651.3208768"},{"doi-asserted-by":"crossref","unstructured":"Nam, Y.-W., Kim, Y.-H.: Melody composition using geometric crossover for variable-length encoding. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 37\u201338 (2017)","key":"15_CR13","DOI":"10.1145\/3067695.3082041"},{"key":"15_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1007\/978-3-319-55750-2_9","volume-title":"Computational Intelligence in Music, Sound, Art and Design","author":"DD Johnson","year":"2017","unstructured":"Johnson, D.D.: Generating polyphonic music using tied parallel networks. In: Correia, J., Ciesielski, V., Liapis, A. (eds.) EvoMUSART 2017. LNCS, vol. 10198, pp. 128\u2013143. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-55750-2_9"},{"unstructured":"Hadjeres, G., Pachet, F.: Deepbach: a steerable model for bach chorales generation. In: Proceedings of the 34th International Conference on Machine Learning, no. 70, pp. 1362\u20131371 (2017)","key":"15_CR15"},{"unstructured":"Syswerda, G.: Uniform crossover in genetic algorithms. In: Proceedings of the Third International Conference on Genetic Algorithms, pp. 2\u20139 (1989)","key":"15_CR16"},{"key":"15_CR17","first-page":"427","volume":"1","author":"A Moraglio","year":"2005","unstructured":"Moraglio, A., Poli, R.: Geometric landscape of homologous crossover for syntactic trees. Evol. Comput. 1, 427\u2013434 (2005)","journal-title":"Evol. Comput."},{"key":"15_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1007\/11729976_11","volume-title":"Genetic Programming","author":"A Moraglio","year":"2006","unstructured":"Moraglio, A., Poli, R., Seehuus, R.: Geometric crossover for biological sequences. In: Collet, P., Tomassini, M., Ebner, M., Gustafson, S., Ek\u00e1rt, A. (eds.) EuroGP 2006. LNCS, vol. 3905, pp. 121\u2013132. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11729976_11"},{"unstructured":"Yoon, Y., Kim, Y.-H., Moraglio, A., Moon, B.-R.: A mathematical unification of geometric crossovers defined on phenotype space. arXiv:0907.3200 (2009)","key":"15_CR19"},{"key":"15_CR20","volume-title":"Windows Forms 2.0 Programming (Microsoft Net Development Series)","author":"C Sells","year":"2006","unstructured":"Sells, C., Weinhardt, M.: Windows Forms 2.0 Programming (Microsoft Net Development Series). Addison-Wesley Professional, Boston (2006)"}],"container-title":["Lecture Notes in Computer Science","Computational Intelligence in Music, Sound, Art and Design"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-16667-0_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T13:52:48Z","timestamp":1710251568000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-16667-0_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030166663","9783030166670"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-16667-0_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"27 March 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EvoMUSART","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Leipzig","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":"24 April 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 April 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"evomusart2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.evostar.org\/2019\/cfp_evomusart.php","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"MyReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"24","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"16","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"67% - 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"}},{"value":"3.3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"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"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}}]}}