Abstract
This chapter suggests a two-way influence between the field of evolutionary and generative music and that of human–computer interaction and usability studies. The interfaces used in evolutionary and generative music can be made more effective and more satisfying to use with the influence of the ideas, methods, and findings of human–computer interaction and usability studies. The musical representations which are a focus of evolutionary and generative music can enable new user-centric tools for mainstream music software. Some successful existing projects are described and some future work is proposed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Baluja, S., Pomerleau, D., & Jochem, T. (1994). Towards automated artificial evolution for computer-generated images. Connection Science, 6(2–3), 325–354.
Biles, J. A. (1994). GenJam: A genetic algorithm for generating jazz solos. In Proceedings of the 1994 international computer music conference (pp. 131–137). Danish Institute of Electroacoustic Music, Denmark: International Computer Music Association.
Boden, M. (2007). What is generative art? COGS seminar.
Collins, N. (2008). The analysis of generative music programs. Organised Sound, 13(3), 237–248.
Collins, T., Laney, R., Willis, A., & Garthwaite, P. (2010). Using discovered, polyphonic patterns to filter computer-generated music. In Proceedings of the international conference on computational creativity, Lisbon, Portugal.
Dahlstedt, P. (2007). Evolution in creative sound design. In E. R. Miranda & J. A. Biles (Eds.), Evolutionary computer music (pp. 79–99). London: Springer.
Dahlstedt, P. (2009). On the role of temporary storage in interactive evolution. In M. Giacobini, et al. (Eds.), Applications of evolutionary computing: EvoWorkshops 2009, no. 5484 in LNCS, (pp. 478–487). New York: Springer.
Dorin, A. (2001). Aesthetic fitness and artificial evolution for the selection of imagery from the mythical infinite library. In J. Kelemen & P. Sosík (Eds.), ECAL 2001 (Vol. 2159 in LNAI). New York: Springer.
Eno, B. (1996). Generative music. http://www.inmotionmagazine.com/eno1.html. Lecture, San Francisco, USA.
Fitts, P. M. (1954). The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology, 47(6), 381–391 (Reprinted in Journal of Experimental Psychology: General, 121(3), 262–269, 1992).
Hoover, A. K., & Stanley, K. O. (2009). Exploiting functional relationships in musical composition. Connection Science, 21(2), 227–251.
Koza, J. R. (1994). Genetic programming II: Automatic discovery of reusable programs. Cambridge, MA: MIT Press.
MacKenzie, I. S. (1992). Fitts’ law as a research and design tool in human–computer interaction. Human–Computer Interaction, 7, 91–139.
Marsden, A. (2005). Generative structural representation of tonal music. Journal of New Music Research, 34(4), 409–428.
McAlpine, K., Miranda, E., & Hoggar, S. (1999). Making music with algorithms: A case-study system. Computer Music Journal, 23(2), 19–30.
McCormack, J. (2008). Evolutionary L-systems. In P. F. Hingston, L. C. Barone, Z. Michalewicz, & D. B. Fogel (Eds.), Design by evolution: Advances in evolutionary design (pp. 169–196). New York: Springer.
McDermott, J., & O’Neill, M. (2010). Higher-order functions in aesthetic EC encodings. In CEC 2010: Proceedings of the 12th annual congress on evolutionary computation (pp. 3018–3025). Barcelona: IEEE Press.
McDermott, J., & O’Reilly, U.-M. (2011). An executable graph representation for evolutionary generative music. In GECCO ’11. Dublin.
McDermott, J., Griffith, N. J. L., & O’Neill, M. (2007a). Evolutionary computation applied to sound synthesis. In J. Romero & P. Machado (Eds.), The art of artificial evolution: A handbook on evolutionary art and music (pp. 81–101). Berlin/Heidelberg: Springer.
McDermott, J., Griffith, N. J. L., & O’Neill, M. (2007b). Evolutionary GUIs for sound synthesis. In M. Giacobini (Ed.), Applications of evolutionary computing (vol. 4448 in LNCS, pp. 547–556). New York: Springer.
McDermott, J., O’Neill, M., & Griffith, N. J. L. (2010). EC control of sound synthesis. Evolutionary Computation Journal, 18(2), 277–303.
McDermott, J., Gifford, T., Bouwer, A., & Wagy, M. (2013). Should music interaction be easy? In S. Holland, K. Wilkie, P. Mulholland, & A. Seago (Eds.), Music and human computer interaction (pp. 29–48). London: Springer.
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. The Psychological Review, 63, 81–97.
O’Neill, M., & Ryan, C. (2003). Grammatical evolution: Evolutionary automatic programming in an arbitrary language. Norwell: Kluwer Academic Publishers.
Schmidhuber, J. (1997). Low-complexity art. Leonardo, 30(2), 97–103.
Seago, A. (2013). A new interaction strategy for musical timbre design. In S. Holland, K. Wilkie, P. Mulholland, & A. Seago (Eds.), Music and human–computer interaction (pp. –). London: Springer. ISBN 978-1-4471-2989-9.
Shao, J., McDermott, J., O’Neill, M., & Brabazon, A. (2010). Jive: A generative, interactive, virtual, evolutionary music system. In Proceedings of EvoWorkshops.
Stowell, D., & McLean, A. (2013). Live music-making: A rich open task requires a rich open interface. In S. Holland, K. Wilkie, P. Mulholland, & A. Seago (Eds.), Music and human–computer interaction (pp. –). London: Springer. ISBN 978-1-4471-2989-9.
Takagi, H. (2001). Interactive evolutionary computation: Fusion of the capabilities of EC optimization and human evaluation. Proceedings of the IEEE, 89(9), 1275–1296.
Whitelaw, M. (2002). Breeding aesthetic objects: Art and artificial evolution. In D. W. Corne & P. J. Bentley (Eds.), Creative evolutionary systems (pp. 129–146). Burlington: Morgan Kaufmann.
Worth, P., & Stepney, S. (2005). Growing music: Musical interpretations of L-systems. In J. Romero & P. Machado (Eds.), EvoMUSART (pp. 545–550). Berlin: Springer.
Acknowledgements
JMcD gratefully acknowledges Inspire funding from the Irish Research Council for Science, Engineering and Technology, co-funded by Marie Curie; DS gratefully acknowledges MIT Undergraduate Research Opportunity (UROP) funding; UMO’R gratefully acknowledges the support of VMWare and USA D.O.E. Grant DE-SC0005288.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag London
About this chapter
Cite this chapter
McDermott, J., Sherry, D., O’Reilly, UM. (2013). Evolutionary and Generative Music Informs Music HCI—And Vice Versa . In: Holland, S., Wilkie, K., Mulholland, P., Seago, A. (eds) Music and Human-Computer Interaction. Springer Series on Cultural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-2990-5_13
Download citation
DOI: https://doi.org/10.1007/978-1-4471-2990-5_13
Published:
Publisher Name: Springer, London
Print ISBN: 978-1-4471-2989-9
Online ISBN: 978-1-4471-2990-5
eBook Packages: Computer ScienceComputer Science (R0)