A Genetic Programming Approach to Generating Musical Compositions

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9027)

Abstract

Evolutionary algorithms have frequently been applied in the field of computer-generated art. In this paper, a novel approach in the domain of automated music composition is proposed. It is inspired by genetic programming and uses a tree-based domain model of compositions. The model represents musical pieces as a set of constraints changing over time, forming musical contexts allowing to compose, reuse and reshape musical fragments. The system implements a multi-objective optimization aiming for statistical measures and structural features of evolved models. Furthermore a correspondent domain-specific computer language is introduced used to transform domain models to a comprehensive, human-readable text representation and vice versa. The language is also suitable to limit the search space of the evolution and as a composition language for human composers.

Keywords

Automated music generation Multi-objective genetic programming Domain-specific languages 

References

  1. 1.
    Collins, N.: Introduction to Computer Music. Wiley, Chichester (2010)Google Scholar
  2. 2.
    Nierhaus, G.: Algorithmic Composition: Paradigms of Automated Music Generation. Springer, New York (2009)CrossRefGoogle Scholar
  3. 3.
    Fogel, D.B.: Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. Wiley, Hoboken (2006)Google Scholar
  4. 4.
    Horner, A., Goldberg, D.E.: Genetic algorithms and computer-assisted music composition. In: Belew, R., Booker, L. (eds.) Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 437–441. Morgan Kaufmann, San Mateo (1991)Google Scholar
  5. 5.
    Biles, J.A.: GenJam: a genetic algorithm for generating jazz solos. In: Proceedings of the 1994 International Computer Music Conference, ICMA, San Francisco, pp. 131–137 (1994)Google Scholar
  6. 6.
    Biles, J.A.: Improvizing with genetic algorithms: GenJam. In: Miranda, E.R., Biles, J.A. (eds.) Evolutionary Computer Music, pp. 137–169. Springer, London (2007)CrossRefGoogle Scholar
  7. 7.
    Horowitz, D.: Generating rhythms with genetic algorithms. In: Proceedings of the 1994 International Computer Music Conference, ICMA, San Francisco, pp. 142–143 (1994)Google Scholar
  8. 8.
    McIntyre, R.A.: Bach in a box: the evolution of four part baroque harmony using the genetic algorithm. In: Proceedings of the IEEE Conference on Evolutionary Computation, vol. 14, No 3. IEEE Press, New York, pp. 852–857 (1994)Google Scholar
  9. 9.
    Horner, A. and Ayers, L.: Harmonization of musical progressions with genetic algorithms. In: Proceedings of the 1995 International Computer Music Conference, ICMA, San Francisco, pp. 483–484 (1995)Google Scholar
  10. 10.
    Jacob, B.: Composing with genetic algorithms. In: Proceedings of the 1995 International Computer Music Conference, ICMA, San Francisco, pp. 452–455 (1995)Google Scholar
  11. 11.
    Jacob, B.: Algorithmic composition as a model of creativity. Organised Sound 1(3), 157–165 (1996)CrossRefGoogle Scholar
  12. 12.
    Johanson, B., Poli, R.: GP-Music: an interactive genetic programming system for music generation with automated fitness raters. In: Koza, J.R., et al. (eds.) Genetic Programming 1998: Proceedings of the Third Annual Conference (GP 1998), pp. 181–186. Morgan Kaufmann, San Francisco (1998)Google Scholar
  13. 13.
    Marques, M., Oliveira, V., Vieira, S., Rosa, A.C.: Music composition using genetic evolutionary algorithms. In: Proceedings of the IEEE Conference on Evolutionary Computation 2000. IEEE Press, New York (2000)Google Scholar
  14. 14.
    de la Puente, A.O., Alfonso, R.S., Moreno, M.A.: Automatic composition of music by means of grammatical evolution. In: Proceedings of the 2002 Conference on APL, pp. 148–155. ACM Press, New York (2002)Google Scholar
  15. 15.
    Fox, C.: Genetic hierarchical music structures. In: Proceedings of the 19th International FLAIRS Conference. AAAI Press, Menlo Park (2006)Google Scholar
  16. 16.
    Waschka II, R.: Composing with genetic algorithms: GenDash. In: Miranda, E.R., Biles, J.A. (eds.) Evolutionary Computer Music, pp. 117–136. Springer, London (2007)CrossRefGoogle Scholar
  17. 17.
    Boden, M.A.: Creativity and computers. In: Dartnall, T. (ed.) Artificial Intelligence and Creativity: An Interdisciplinary Approach, pp. 3–26. Kluwer Academic Publishers, Dordrecht (1994)CrossRefGoogle Scholar
  18. 18.
    Boden, M.A.: Creativity and Art: Three Roads to Surprise. Oxford University Press, Oxford (2010)Google Scholar
  19. 19.
    Deza, M.M., Deza, E.: Encyclopedia of Distances. Springer, Heidelberg (2013)CrossRefMATHGoogle Scholar
  20. 20.
    Barlow, C.: On musiquantics. Technical report, Johannes Gutenberg-Universität Mainz (2012)Google Scholar
  21. 21.
    Patel, A.D.: Music, Language, and the Brain. Oxford University Press, New York (2008)Google Scholar
  22. 22.
    Poli, R., Langdon, W.B., McPhee, N.F., Koza, J.R.: A Field Guide to Genetic Programming (2008). Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Institute for Musicology and Music InformaticsUniversity of Music KarlsruheKarlsruheGermany

Personalised recommendations