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An optimization algorithm inspired by musical composition

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Abstract

In this paper we propose a new multiagent metaheuristic based in an artificial society that uses a dynamic creative system to compose music, called “Method of musical composition” or MMC. To show the performance of our proposed MMC algorithm, 13 benchmark continuous optimization problems and the related results are compared with harmony search, improved harmony search, global-best harmony search and self-adaptative harmony search. The experimental results demonstrate that MMC improves the results obtained by the other metaheuristics in a set of multi-modal functions.

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References

  • Ali MM, Khompatraporn C, Zabinsky ZB (2005) A numerical evaluation of several stochastic algorithms on selected continuous global optimization test problems. J Global Optim 31: 635–672

    Article  MATH  MathSciNet  Google Scholar 

  • Bersini H, Dorigo M, Langerman S, Seront G, Gambardella LM (1996) Results of the first international contest on evolutionary optimisation (1st iceo). International conference on evolutionary computation, pp 611–615. http://dblp.uni-trier.de

  • Biles JA (1994) Genjam: a genetic algorithm for generating jazz solos. International computer music conference. International Computer Music Association, Aarhus, pp 131–137

  • Brest J, Greiner S, Boskovic B, Mernik M, Zumer V (2006) Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE Trans Evol Comput 10: 646–657

    Article  Google Scholar 

  • Chelouaha R, Siarry P (2000) Tabu search applied to global optimization. Euro J Oper Res 23: 256–270

    Article  Google Scholar 

  • Cope D (2000) The algorithmic composer. A-R Editions Inc., Wisconsin

    Google Scholar 

  • Cope D (2005) Computer model of musical creativity. MIT Press, London

    Google Scholar 

  • de Bono E (1993) El pensamiento práctico, Editorial Paidos

  • de los Cobos Silva SG, Close JG, Andrade MAG, Licona AEM (2010) Búsqueda y exploración estocástica. Universidad Autónoma Metropolitana, Mexico

    Google Scholar 

  • Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybernet 26: 29–41

    Article  Google Scholar 

  • Dréo J, Pétrowski A, Siarry P, Taillard E (2006) Metaheuristics for hard optimization: methods and case studies. Springer, Berlin

    Google Scholar 

  • Geem ZW (2009) Recent advances in harmony search algorithm. Springer, Berlin

    Book  Google Scholar 

  • Geem ZW (2010) Music-inspired harmony search algorithm. Springer, USA

    Book  Google Scholar 

  • Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2): 60–68

    Article  Google Scholar 

  • Gessler N (2010) Fostering creative emergences in artificial cultures. In: Artificial life XII—proceedings of the twelfth international conference on the synthesis and simulation of living systems, MIT Press, pp 669–676

  • Heller K, Mönks F, Csikszentmihalyi M, Wolfe R (2000) The international handbook of giftedness and talent. Elsevier, New York

    Google Scholar 

  • Horner A, Goldberg DE (1991) Genetic algorithms and computer assisted music composition. Music composition. In: ICMC91 proceedings, International Computer Music Association, San Francisco, pp 479–482

  • Jacob B (1995) Composing with genetic algorithms. International Computer Music Association, San Francisco, pp 452–455

    Google Scholar 

  • Jacob BL (1996) Algorithmic composition as a model of creativity. Organ Sound 1: 157–165

    Article  Google Scholar 

  • Joshi MC, Moudgalya KM (2004) Optimization: theory and practice. Alpha Science International, Ltd., UK

    Google Scholar 

  • Kenedy J, Eberhart RC (1995) Particle swarm optimization. International Conference Neuronal Networks, UK, pp 1942–1948

    Google Scholar 

  • Lee KS, Geem ZW (2004) A new structural optimization method based on the harmony search algorithm. Comput struct 82: 781–798

    Article  Google Scholar 

  • Lee KS, Geem ZW (2005) A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput Methods Appl Mech Eng 194: 3902–3933

    Article  MATH  Google Scholar 

  • Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10: 281–295

    Article  Google Scholar 

  • Liu YT (2000) Creativity or novelty? Cognitive-computational versus social-cultural. Design Stud 23: 261–276

    Article  Google Scholar 

  • Luenberger DG (1984) Linear and nonlinear programming. Addison-Wesley, Boston

    MATH  Google Scholar 

  • Molga M, Smutnicki C (2005) Test functions for optimization needs. http://www.zsd.ict.pwr.wroc.pl/files/docs/functions.pdf

  • Pan QK, Suganthan PN, Tasgetiren MF, Liang JJ (2010) A self-adaptive global best harmony search algorithm for continuous optimization problems. Appl Math Comput 216: 830–848

    Article  MATH  MathSciNet  Google Scholar 

  • Pohlheim H (2006) Geatbx: genetic and evolutionary algorithm toolbox for use with matlab. http://www.geatbx.com/

  • Ray T, Liew KM (2003) Society and civilization: an optimization algorithm based on simulation of social behavior. IEEE Trans Evol Comput 7: 386–396

    Article  Google Scholar 

  • Reynolds RG (1994) An introduction to cultural algorithms. In: Proceedings of the 3rd annual conference on evolutionary programming,World Scientific Publishing, pp 131–139

  • Riley MJW, Jenkins KW, Thompson CP (2010) A study of early stopping, ensembling, and patchworking for cascade correlation neural networks. IAENG Int J Appl Math 40(4): 307–316

    Google Scholar 

  • Salomon R (1996) Reevaluating genetic algorithm performance under coordinate rotation of benchmark functions; a survey of some theoretical and practical aspects of genetic algorithms. BioSystems 39: 263–278

    Article  Google Scholar 

  • Wang CM, Huang YF (2010) Self-adaptive harmony search algorithm for optimization. Expert Syst Appl 37: 2826–2837

    Article  Google Scholar 

  • Yang XS (2010) Test problems in optimization. Engineering optimization: an introduction with metaheuristic applications. Wiley, NJ

    Book  Google Scholar 

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Correspondence to Roman Anselmo Mora-Gutiérrez.

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Mora-Gutiérrez, R.A., Ramírez-Rodríguez, J. & Rincón-García, E.A. An optimization algorithm inspired by musical composition. Artif Intell Rev 41, 301–315 (2014). https://doi.org/10.1007/s10462-011-9309-8

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