IDEAS home Printed from https://ideas.repec.org/p/ant/wpaper/2012020.html
   My bibliography  Save this paper

Composing Fifth Species Counterpoint Music With Variable Neighborhood Search

Author

Listed:
  • HERREMANS, Dorien
  • SÖRENSEN, Kenneth

Abstract

In this paper, a variable neighborhood search (VNS) algorithm is developed and analyzed that can generate fifth species counterpoint fragments. The existing species counterpoint rules are quantified and form the basis of the objective function used by the algorithm. The VNS developed in this research is a local search metaheuristic that starts from a randomly generated fragment and gradually improves this solution by changing one or two notes at a time. An in-depth statistical analysis reveals the significance as well as the optimal settings of the parameters of the VNS. The algorithm has been implemented in a user-friendly software environment called Optimuse. Optimuse allows a user to input basic characteristics such as length, key and mode. Based on this input, a fifth species counterpoint fragment is generated that can be edited and played back immediately. This work is the expansion of a previous paper by the authors in which first species counterpoint music is composed by a similar VNS algorithm.

Suggested Citation

  • HERREMANS, Dorien & SÖRENSEN, Kenneth, 2012. "Composing Fifth Species Counterpoint Music With Variable Neighborhood Search," Working Papers 2012020, University of Antwerp, Faculty of Business and Economics.
  • Handle: RePEc:ant:wpaper:2012020
    as

    Download full text from publisher

    File URL: https://repository.uantwerpen.be/docman/irua/93e44d/cff18030.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Avanthay, Cedric & Hertz, Alain & Zufferey, Nicolas, 2003. "A variable neighborhood search for graph coloring," European Journal of Operational Research, Elsevier, vol. 151(2), pages 379-388, December.
    2. Fleszar, Krzysztof & Hindi, Khalil S., 2004. "Solving the resource-constrained project scheduling problem by a variable neighbourhood search," European Journal of Operational Research, Elsevier, vol. 155(2), pages 402-413, June.
    3. Aguilera, Gabriel & Luis Galán, José & Madrid, Rafael & Martínez, Antonio Manuel & Padilla, Yolanda & Rodríguez, Pedro, 2010. "Automated generation of contrapuntal musical compositions using probabilistic logic in Derive," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 80(6), pages 1200-1211.
    4. Hansen, Pierre & Mladenovic, Nenad, 2001. "Variable neighborhood search: Principles and applications," European Journal of Operational Research, Elsevier, vol. 130(3), pages 449-467, May.
    5. Olli Bräysy, 2003. "A Reactive Variable Neighborhood Search for the Vehicle-Routing Problem with Time Windows," INFORMS Journal on Computing, INFORMS, vol. 15(4), pages 347-368, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. HERREMANS, Dorien & SÖRENSEN, Kenneth, 2013. "FuX, an android app that generates counterpoint," Working Papers 2013003, University of Antwerp, Faculty of Business and Economics.
    2. Pierre Hansen & Nenad Mladenović & José Moreno Pérez, 2010. "Variable neighbourhood search: methods and applications," Annals of Operations Research, Springer, vol. 175(1), pages 367-407, March.
    3. VÁZQUEZ-ALCOCER, Alan & GOOS, Peter & SCHOEN, Eric D., 2016. "Two-level designs constructed by concatenating orthogonal arrays of strenght three," Working Papers 2016011, University of Antwerp, Faculty of Business and Economics.
    4. HERREMANS, Dorien & WEISSER, Stéphanie & SÖRENSEN, Kenneth & CONKLIN, Darrell, 2014. "Generating structured music using quality metrics based on Markov models," Working Papers 2014019, University of Antwerp, Faculty of Business and Economics.
    5. Liang, Yun-Chia & Chen, Yi-Ching, 2007. "Redundancy allocation of series-parallel systems using a variable neighborhood search algorithm," Reliability Engineering and System Safety, Elsevier, vol. 92(3), pages 323-331.
    6. Ayob, Masri & Kendall, Graham, 2008. "A survey of surface mount device placement machine optimisation: Machine classification," European Journal of Operational Research, Elsevier, vol. 186(3), pages 893-914, May.
    7. SYAFITRI, Utami & SARTONO, Bagus & GOOS, Peter, 2015. "D- and I-optimal design of mixture experiments in the presence of ingredient availability constraints," Working Papers 2015003, University of Antwerp, Faculty of Business and Economics.
    8. H-Y Lin & C-J Liao & C-T Tseng, 2011. "An application of variable neighbourhood search to hospital call scheduling of infant formula promotion," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(6), pages 949-959, June.
    9. G I Zobolas & C D Tarantilis & G Ioannou, 2009. "A hybrid evolutionary algorithm for the job shop scheduling problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(2), pages 221-235, February.
    10. GARROI, Jean-Jacques & GOOS, Peter & SÖRENSEN, Kenneth, 2006. "A variable-neighbourhood search algorithm for finding optimal run orders in the presence of serial correlation and time trends," Working Papers 2006026, University of Antwerp, Faculty of Business and Economics.
    11. Hemmelmayr, Vera C. & Doerner, Karl F. & Hartl, Richard F., 2009. "A variable neighborhood search heuristic for periodic routing problems," European Journal of Operational Research, Elsevier, vol. 195(3), pages 791-802, June.
    12. Shahram Shahinpour & Sergiy Butenko, 2013. "Algorithms for the maximum k-club problem in graphs," Journal of Combinatorial Optimization, Springer, vol. 26(3), pages 520-554, October.
    13. Maenhout, Broos & Vanhoucke, Mario, 2010. "A hybrid scatter search heuristic for personalized crew rostering in the airline industry," European Journal of Operational Research, Elsevier, vol. 206(1), pages 155-167, October.
    14. Drezner, Zvi & Eiselt, H.A., 2024. "Competitive location models: A review," European Journal of Operational Research, Elsevier, vol. 316(1), pages 5-18.
    15. Maud Bay & Yves Crama & Yves Langer & Philippe Rigo, 2010. "Space and time allocation in a shipyard assembly hall," Annals of Operations Research, Springer, vol. 179(1), pages 57-76, September.
    16. Chen, Qingfeng & Li, Kunpeng & Liu, Zhixue, 2014. "Model and algorithm for an unpaired pickup and delivery vehicle routing problem with split loads," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 69(C), pages 218-235.
    17. Hanen Akrout & Bassem Jarboui & Patrick Siarry & Abdelwaheb Rebaï, 2012. "A GRASP based on DE to solve single machine scheduling problem with SDST," Computational Optimization and Applications, Springer, vol. 51(1), pages 411-435, January.
    18. Manlio Gaudioso & Giovanni Giallombardo & Giovanna Miglionico, 2018. "Minimizing Piecewise-Concave Functions Over Polyhedra," Mathematics of Operations Research, INFORMS, vol. 43(2), pages 580-597, May.
    19. Paola Pellegrini & Lorenzo Castelli & Raffaele Pesenti, 2011. "Metaheuristic algorithms for the simultaneous slot allocation problem," Working Papers 9, Venice School of Management - Department of Management, Università Ca' Foscari Venezia.
    20. Federico Della Croce & Andrea Grosso & Fabio Salassa, 2014. "A matheuristic approach for the two-machine total completion time flow shop problem," Annals of Operations Research, Springer, vol. 213(1), pages 67-78, February.

    More about this item

    Keywords

    Variable Neighborhood Search (VNS); Metaheuristics; Local search; Music; Computer Aided Composition (CAC);
    All these keywords.

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ant:wpaper:2012020. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joeri Nys (email available below). General contact details of provider: https://edirc.repec.org/data/ftufsbe.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.