Abstract
This paper analyzes the difficulty of finding solutions for the 4-part harmony problem, both from the point of view of the size of the search space and the time required to run the algorithm. These considerations led to improved running time through parallelization, precalculation of the fitness function, and directed mutation, which reduces the time to solution. Moreover, we show how combining these techniques allows us to extend the improvements to different harmony models, including new synthetic ones that may be proposed.
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References
Lopez-Rincon, O., Starostenko, O., Ayala-San Martin, G.: Algoritmic music Composition based on artificial intelligence: a survey. In: International Conference on Electronics, Communications and Computers (CONIELECOMP), pp. 187–193 (2018). https://doi.org/10.1109/CONIELECOMP.2018.8327197
Liu, C.H., Ting, C.K.: Computational intelligence in music composition: a survey. IEEE Trans. Emerg. Top. Comput. Intell. 1(2), 2–15 (2017)
de Vega, F.F.: Revisiting the 4-part harmonization problem with GAs: a critical review and proposals for improving. In: IEEE Congress on Evolutionary Computation (CEC), Donostia, Spain, pp. 1271–1278 (2017). https://doi.org/10.1109/CEC.2017.7969451
Fernandez De Vega, F., Alvarado, J., Sanchez, A., Serrano, M., Pacioni, E.: Evolutionary algorithms: a new hope for the future of music teaching. In: Proceedings of the Companion Conference on Genetic and Evolutionary Computation (GECCO ’23 Companion), pp. 65–66. Association for Computing Machinery, New York, NY, USA (2023). https://doi.org/10.1145/3583133.3596945
Horner, A., Goldberg, D.E.: Genetic algorithms and computer-assisted music composition. In: International Conference on Mathematics and Computing, pp. 437–441 (1991)
McIntyre, R.A.: Bach in a box: the evolution of four part baroque harmony using the genetic algorithm. In: Evolutionary Computation: IEEE World Congress on Computational Intelligence. Proceedings of the First IEEE Conference 1994, pp. 852–857 (1994). https://doi.org/10.1109/ICEC.1994.349943
Kaliakatsos-Papakostas, M., Cambouropoulos, E.: Probabilistic harmonization with fixed intermediate chord constraints. In: ICMC (2014). https://doi.org/10.13140/2.1.3079.5526
Jin, Y.: A comprehensive survey of fitness approximation in evolutionary computation. Soft Comput. 9(1), 3–12 (2005). https://doi.org/10.1007/s00500-003-0328-5
Shi, L., Rasheed, K.: A Survey of Fitness Approximation Methods Applied in Evolutionary Algorithms. Computational Intel. in Expensive Opti. Prob., ALO, 2 (2010)
Hosder, S., et al.: Polynomial response surface approximations for the multidisciplinary design optimization of a high speed civil transport. Optim. Eng. 2, 431–452 (2001)
Bueche, D., Schraudolph, N.N., Koumoutsakos, P.: Accelerating evolutionary algorithms with gaussian process fitness function models. IEEE Trans. Syst. Man Cybern. Part C 2(35), 183–194 (2005). https://doi.org/10.1109/tsmcc.2004.841917
Ulmer, H., Streichert, F., Zell, A.: Model-assisted evolution strategies. Knowl. Inc. Evolutionary Comput. 333–355 (2005)
Melamed, D., et al.: De novo mutation rates at the single-mutation resolution in a human HBB gene-region associated with adaptation and genetic disease. Genome Res. (2022). https://doi.org/10.1101/gr.276103.121
Bhandari, D., Pal, N.R., Pal, S.K.: Directed mutation in genetic algorithms. Inf. Sci. 79(3–4), 251–270 (1994). https://doi.org/10.1016/0020-0255(94)90123-6
Tang, P.-H., Tseng, M.-H.: Adaptive directed mutation for real-coded genetic algorithms. Appl. Soft Comput. 13(1), 600–614 (2013). https://doi.org/10.1016/j.asoc.2012.08.035
Puerta, B.R., Barrancas, F.D., Chavez, F., de Vega, F.F.: Un análisis preliminar de nuevos modelos de mutación dirigida en algoritmos genéticos. In: Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2018), Granada, España
Carvalho, P., Magane, J., Lourenco, N., Machado, P.: Context matters: adaptive mutation for grammars. In: Pappa, G., Giacobini, M., Vasicek, Z. (eds.) Genetic Programming. EuroGP 2023. LNCS, vol. 13986, pp. 117–132 . Springer, Cham (2023). https://doi.org/10.1007/978-3-031-29573-7_8
Acknowledgements
We acknowledge support from Spanish Ministry of Economy and Competitiveness under projects PID2020-115570GB-C21 funded by MCIN/AEI/10.13039/501100011033. Junta de Extremadura under project GR15068.
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Pacioni, E., Fernández De Vega, F. (2024). On the Impact of Directed Mutation Applied to Evolutionary 4-Part Harmony Models. In: Johnson, C., Rebelo, S.M., Santos, I. (eds) Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2024. Lecture Notes in Computer Science, vol 14633. Springer, Cham. https://doi.org/10.1007/978-3-031-56992-0_20
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