Skip to main content
SpringerLink
Log in
Book cover

Handbook of Grammatical Evolution pp 341–366Cite as

Grammatical Evolution and Creativity

  • Chapter
  • First Online:

Abstract

This paper considers the application of Grammatical Evolution (GE) to the concept of creativity both in theory and through the examination of two applied music generation systems. We discuss previous work on the application of evolutionary strategies to music generation and discuss current issues in the study of creativity and Computational Creativity (CC). In presenting and contrasting the development of two GE music generation systems, we can consider the multi-faceted aspects of creativity and how it may be approached from a computational perspective. The design of any such system is dependent on representation (what is music?) and fitness measure (what makes this music good?). In any aesthetic domain such questions are far from trivial. We conclude that it is vitally important to be clear on the purpose and aim in proposing any such system; systems may be either more generative or more autonomously creative if this is the a priori goal of the proposed experiment. Furthermore, we propose that evolutionary systems, and in particular GE, are highly suitable to the study of creativity as they can offer much scope in representation through grammars while allowing exploration and the possibility of self-adaptivity through the development of novel self-referential fitness measures.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    https://soundcloud.com/user-529879178/sets/composingponymelodies.

References

  1. J. Biles, GenJam: a genetic algorithm for generating jazz solos, in Proceedings of the International Computer Music Conference (International Computer Music Association, San Francisco, 1994), pp. 131–131

    Google Scholar 

  2. J.A. Biles, Straight-ahead jazz with GenJam: a quick demonstration, in MUME 2013 Workshop (2013)

    Google Scholar 

  3. M.A. Boden, Creativity and artificial intelligence. Artif. Intell. 103(1), 347–356 (1998)

    Article  MathSciNet  Google Scholar 

  4. M.A. Boden, The Creative Mind: Myths and Mechanisms (Psychology Press, London, 2004)

    Book  Google Scholar 

  5. M.A. Boden, Computer models of creativity. AI Mag. 30(3), 23 (2009)

    Google Scholar 

  6. S. Bringsjord, P. Bello, D. Ferrucci, Creativity, the turing test, and the (better) lovelace test, in The Turing Test (Springer, Berlin, 2003), pp. 215–239

    MATH  Google Scholar 

  7. A.R. Brown, A. Sorensen, Interacting with generative music through live coding. Contemp. Music. Rev. 28(1), 17–29 (2009)

    Article  Google Scholar 

  8. A. Cardoso, T. Veale, G.A. Wiggins, Converging on the divergent: the history (and future) of the international joint workshops in computational creativity. AI Mag. 30(3), 15 (2009)

    Google Scholar 

  9. M.A. Castellano, J.J. Bharucha, C.L. Krumhansl, Tonal hierarchies in the music of north india. J. Exp. Psychol. Gen. 113(3), 394 (1984)

    Google Scholar 

  10. N. Collins, A. McLean, J. Rohrhuber, A. Ward, Live coding in laptop performance. Organised Sound 8(3), 321 (2003)

    Google Scholar 

  11. F. Colombo, A. Seeholzer, W. Gerstner, Deep artificial composer: a creative neural network model for automated melody generation, in International Conference on Evolutionary and Biologically Inspired Music and Art (Springer, Berlin, 2017), pp. 81–96

    Google Scholar 

  12. S. Colton, G.A. Wiggins, et al., Computational creativity: the final frontier? in Proceedings of the 20th European Conference on Artificial Intelligence ECAI’12, vol. 12 (2012), pp. 21–26

    Google Scholar 

  13. M. Cook, S. Colton, Generating code for expressing simple preferences: moving on from hardcoding and randomness, in Proceedings of the Sixth International Conference on Computational Creativity (2015), p. 8

    Google Scholar 

  14. P. Dahlstedt, Autonomous evolution of complete piano pieces and performances, in Proceedings of Music AL Workshop. Citeseer (2007)

    Google Scholar 

  15. A.R. de Freitas, F.G. Guimaraes, R.V. Barbosa, Ideas in automatic evaluation methods for melodies in algorithmic composition, in Sound and Music Computing Conference (2012)

    Google Scholar 

  16. A.O. de la Puente, R.S. Alfonso, M.A. Moreno, Automatic composition of music by means of grammatical evolution, in ACM SIGAPL APL Quote Quad, vol. 32 (ACM, New York, 2002), pp. 148–155

    Google Scholar 

  17. A. Eigenfeldt, P. Pasquier, Populations of populations: composing with multiple evolutionary algorithms, in Evolutionary and Biologically Inspired Music, Sound, Art and Design (Springer, Berlin, 2012), pp. 72–83

    Google Scholar 

  18. A. Eigenfeldt, O. Bown, A.R. Brown, T. Gifford, Flexible generation of musical form: beyond mere generation, in Proceedings of the Seventh International Conference on Computational Creativity (2016)

    Google Scholar 

  19. M. Fenton, C. McNally, J. Byrne, E. Hemberg, J. McDermott, M. O’Neill, Automatic innovative truss design using grammatical evolution. Autom. Constr. 39, 59–69 (2014)

    Article  Google Scholar 

  20. J.D. Fernández, F. Vico, AI methods in algorithmic composition: a comprehensive survey. J. Artif. Intell. Res. 48, 513–582 (2013)

    Article  MathSciNet  Google Scholar 

  21. C. Guckelsberger, C. Salge, S. Colton, Addressing the “why?” in computational creativity: a non-anthropocentric, minimal model of intentional creative agency, in Proceedings of the 8th International Conference on Computational Creativity, Atlanta (2017)

    Google Scholar 

  22. M. Harman, S.A. Mansouri, Y. Zhang, Search-based software engineering: trends, techniques and applications. ACM Comput. Surv. 45(1), 11 (2012)

    Google Scholar 

  23. S. Hickinbotham, S. Stepney, Augmenting live coding with evolved patterns, in International Conference on Evolutionary and Biologically Inspired Music and Art (Springer, Berlin, 2016), pp. 31–46

    Google Scholar 

  24. K.E. Jennings, Developing creativity: artificial barriers in artificial intelligence. Mind. Mach. 20(4), 489–501 (2010)

    Article  Google Scholar 

  25. A. Jordanous, Evaluating evaluation: assessing progress in computational creativity research, in Proceedings of the 2nd International Conference on Computational Creativity (ICCC-11), Mexico City, Mexico (2011)

    Google Scholar 

  26. A. Jordanous, A standardised procedure for evaluating creative systems: computational creativity evaluation based on what it is to be creative. Cogn. Comput. 4(3), 246–279 (2012)

    Article  Google Scholar 

  27. A. Jordanous, B. Keller, Modelling creativity: identifying key components through a corpus-based approach. PloS one 11(10), e0162959 (2016)

    Google Scholar 

  28. R. Kicinger, T. Arciszewski, K. De Jong, Evolutionary computation and structural design: a survey of the state-of-the-art. Comput. Struct. 83(23), 1943–1978 (2005)

    Article  Google Scholar 

  29. C.L. Krumhansl, Tonality induction: a statistical approach applied cross-culturally. Music. Percept. 17(4), 461–479 (2000)

    Article  Google Scholar 

  30. C.L. Krumhansl, L.L. Cuddy, A theory of tonal hierarchies in music, in Music Perception (Springer, Berlin, 2010), pp. 51–87

    Book  Google Scholar 

  31. F. Lerdahl, R. Jackendoff, A Generative Theory of Tonal Music (MIT Press, Cambridge, 1985)

    Google Scholar 

  32. M. Lewis, Evolutionary visual art and design, in The Art of Artificial Evolution (Springer, Berlin, 2008), pp. 3–37

    Book  Google Scholar 

  33. R. Loughran, M. O’Neill, Generative music evaluation: why do we limit to ‘human’? in Computer Simulation of Musical Creativity (CSMC). Huddersfield, UK (2016)

    Google Scholar 

  34. R. Loughran, M. O’Neill, Application domains considered in computational creativity, in Proceedings of the 8th International Conference on Computational Creativity, Atlanta (2017)

    Google Scholar 

  35. R. Loughran, M. O’Neill, Clustering agents for the evolution of autonomous musical fitness, in Evolutionary and Biologically Inspired Music, Sound, Art and Design (Springer, Berlin, 2017)

    Google Scholar 

  36. R. Loughran, J. McDermott, M. O’Neill, Grammatical evolution with zipf’s law based fitness for melodic composition, in Sound and Music Computing Conference, Maynooth (2015)

    Google Scholar 

  37. N. Lourenço, F. Assunção, C. Maçãs, P. Machado, Evofashion: customising fashion through evolution, in International Conference on Evolutionary and Biologically Inspired Music and Art (Springer, Berlin, 2017), pp. 176–189

    Google Scholar 

  38. J. McCormack, Grammar based music composition. Complex Syst. 96, 321–336 (1996)

    Google Scholar 

  39. E.R. Miranda, J. Al Biles, Evolutionary Computer Music (Springer, Berlin, 2007)

    Book  Google Scholar 

  40. E. Munoz, J. Cadenas, Y.S. Ong, G. Acampora, Memetic music composition. IEEE Trans. Evol. Comput. 20(1), 1–15 (2016)

    Article  Google Scholar 

  41. H.G. Oliveira, Poetryme: a versatile platform for poetry generation. Comput. Creat. Concept Invent. Gen. Intell. 6(1), 21 (2012)

    Google Scholar 

  42. F. Pachet, The continuator: musical interaction with style. J. N. Music Res. 32(3), 333–341 (2003)

    Article  Google Scholar 

  43. J. Reddin, J. McDermott, M. O’Neill, Elevated pitch: automated grammatical evolution of short compositions, in Applications of Evolutionary Computing (Springer, Berlin, 2009), pp. 579–584

    Google Scholar 

  44. G. Ritchie, The transformational creativity hypothesis. N. Gener. Comput. 24(3), 241–266 (2006)

    Article  MathSciNet  Google Scholar 

  45. M. Scirea, J. Togelius, P. Eklund, S. Risi, Metacompose: a compositional evolutionary music composer, in International Conference on Evolutionary and Biologically Inspired Music and Art (Springer, Berlin, 2016), pp. 202–217

    Google Scholar 

  46. J.R. Searle, Minds, brains, and programs. Behav. Brain Sci. 3(3), 417–424 (1980)

    Article  Google Scholar 

  47. J.R. Searle, Is the brain a digital computer? The American Philosophical Association Centennial Series (2013), pp. 691–710

    Google Scholar 

  48. J. Shao, J. McDermott, M. O’Neill, A. Brabazon, Jive: a generative, interactive, virtual, evolutionary music system, in Applications of Evolutionary Computation (Springer, Berlin, 2010), pp. 341–350

    Google Scholar 

  49. D. Temperley, E.W. Marvin, Pitch-class distribution and the identification of key. Music. Percept. 25, 193–212 (2008)

    Article  Google Scholar 

  50. D. Ventura, Mere generation: essential barometer or dated concept, in Proceedings of the Seventh International Conference on Computational Creativity, ICCC (2016)

    Google Scholar 

  51. D. Ventura, How to build a CC system, in Proceedings of the 8th International Conference on Computational Creativity, Atlanta (2017)

    Google Scholar 

  52. G. Wang, P.R. Cook, et al., Chuck: a concurrent, on-the-fly, audio programming language, in Proceedings of the 2003 International Computer Music Conference ICMC (2003)

    Google Scholar 

  53. R. Waschka II, Composing with genetic algorithms: GenDash, in Evolutionary Computer Music (Springer, Berlin, 2007), pp. 117–136

    Google Scholar 

  54. G.A. Wiggins, D. Müllensiefen, M.T. Pearce, On the non-existence of music: why music theory is a figment of the imagination. Music. Sci. 14(1 suppl), 231–255 (2010)

    Article  Google Scholar 

Download references

Acknowledgements

This work is part of the App’Ed (Applications of Evolutionary Design) project funded by Science Foundation Ireland under grant 13/IA/1850.

Author information

Authors and Affiliations

  1. Natural Computing Research and Applications Group (NCRA), Michael Smurfit Graduate Business School, UCD, Dublin, Ireland

    Róisín Loughran

Authors
  1. Róisín Loughran
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Róisín Loughran .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Information Systems, University of Limerick, Castletroy, Limerick, Ireland

    Conor Ryan

  2. School of Business, University College Dublin, Dublin, Ireland

    Michael O'Neill

  3. Department of Computer Science and Information Systems, University of Limerick, Castletroy, Limerick, Ireland

    JJ Collins

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Loughran, R. (2018). Grammatical Evolution and Creativity. In: Ryan, C., O'Neill, M., Collins, J. (eds) Handbook of Grammatical Evolution. Springer, Cham. https://doi.org/10.1007/978-3-319-78717-6_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-78717-6_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-78716-9

  • Online ISBN: 978-3-319-78717-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation