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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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
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
Chelouaha R, Siarry P (2000) Tabu search applied to global optimization. Euro J Oper Res 23: 256–270
Cope D (2000) The algorithmic composer. A-R Editions Inc., Wisconsin
Cope D (2005) Computer model of musical creativity. MIT Press, London
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
Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybernet 26: 29–41
Dréo J, Pétrowski A, Siarry P, Taillard E (2006) Metaheuristics for hard optimization: methods and case studies. Springer, Berlin
Geem ZW (2009) Recent advances in harmony search algorithm. Springer, Berlin
Geem ZW (2010) Music-inspired harmony search algorithm. Springer, USA
Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2): 60–68
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
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
Jacob BL (1996) Algorithmic composition as a model of creativity. Organ Sound 1: 157–165
Joshi MC, Moudgalya KM (2004) Optimization: theory and practice. Alpha Science International, Ltd., UK
Kenedy J, Eberhart RC (1995) Particle swarm optimization. International Conference Neuronal Networks, UK, pp 1942–1948
Lee KS, Geem ZW (2004) A new structural optimization method based on the harmony search algorithm. Comput struct 82: 781–798
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
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
Liu YT (2000) Creativity or novelty? Cognitive-computational versus social-cultural. Design Stud 23: 261–276
Luenberger DG (1984) Linear and nonlinear programming. Addison-Wesley, Boston
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
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
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
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
Wang CM, Huang YF (2010) Self-adaptive harmony search algorithm for optimization. Expert Syst Appl 37: 2826–2837
Yang XS (2010) Test problems in optimization. Engineering optimization: an introduction with metaheuristic applications. Wiley, NJ
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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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DOI: https://doi.org/10.1007/s10462-011-9309-8