Evolving Musical Harmonisation

  • Somnuk Phon-Amnuaisuk
  • Andrew Tuson
  • Geraint Wiggins


We describe a series of experiments in generating traditional musical harmony using Genetic Algorithms. We discuss some problems which are specific to the musical domain, and conclude that a GA with no notion of metalevel control of the reasoning process is unlikely to solve the harmonisation problem well.


Genetic Algorithm Fitness Function Fitness Landscape Genetic Programming System Computer Music 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    J. A. Biles. GenJam: A genetic algorithm for generating jazz solos. In ICMC Proceedings 1994. The Computer Music Association, 1994.Google Scholar
  2. [2]
    E. Burke, D. Elliman, and R. Weare. The Automated Timetabling of University Exams using a Hybrid Genetic Algorithm. In The AISB Workshop on Evolutionary Computing, 1995.Google Scholar
  3. [3]
    A. R. Burton and T. Vladimirova. Applications of genetic techniques to musical composition, 1997. Available by WWW at
  4. [4]
    A. R. Burton and T. Vladimirova. A genetic algorithm for utilising neural network fitness evaluation for musical composition. In Proceedingsof the 1997 International Conference on Artificial Neural Networks and Genetic Algorithms, pages 220–224, 1997.Google Scholar
  5. [5]
    L. Davis, editor. Handbook of Genetic Algorithms. New York: Van Nostrand Reinhold, 1991.Google Scholar
  6. [6]
    David E. Goldberg and Jon Richardson. Genetic algorithms with sharing for multimodal function optimization. In Proceedings of the Second International Conference on Genetic Algorithms and Their Applications, pages 41–49. San Mateo: Morgan Kaufmann, 1987.Google Scholar
  7. [7]
    VS. Gordon, D. Whitley, and A. Böhn. Dataflow parallelism in genetic algorithms. In R. Männer and B. Manderick, editors, Parallel Problem Solving from Nature 2, pages 553–42, Amsterdam, 1992. Elsevier Science.Google Scholar
  8. [8]
    G. R. Harik and D. E. Goldberg. Learning linkage. In Foundations of Genetic Algorithms IV, pages 270–85. San Mateo: Morgan Kaufmann, 1996.Google Scholar
  9. [9]
    A. Horner and L. Ayers. Harmonisation of musical progression with genetic algorithms. In ICMC Proceedings 1995, pages 483–484. The Computer Music Association, 1995.Google Scholar
  10. [10]
    A. Horner and D. E. Goldberg. Genetic algorithms and computer-assisted music composition. Technical report, University of Illinois, December 1991.Google Scholar
  11. [11]
    B. L. Jacob. Composing with genetic algorithms. Technical report, University of Michigan, September 1995.Google Scholar
  12. [12]
    B. Johanson and R. Poli. Gp-music: An interactive genetic programming system for music generation with automated fitness raters. In Proceedings of the 3rd International Conference on Genetic Programming, GP′98. MIT Press, 1998.Google Scholar
  13. [13]
    R. A. Mclntyre. Bach in a box: The evolution of four-part baroque harmony using a genetic algorithm. In First IEEE Conference on Evolutionary Computation, pages 852–857. 1994.Google Scholar
  14. [14]
    L. Spector and A. Alpern. Criticism, culture, and the automatic generation of artworks. In Proceedings of the 12th National Conference on Artificial Intelligence, 1994.Google Scholar
  15. [15]
    E. Taylor. The AB Guide to Music Theory. The Associated Board of the Royal Schools of Music, London, 1996.Google Scholar
  16. [16]
    G. A. Wiggins, M. Harris, and A. Smaill. Representing music for analysis and composition. In M. Balaban, K. Ebcioglu, O. Laske, C. Lischka, and L. Sorisio, editors, Proceedings of the 2nd IJCAI AI/Music Workshop, pages 63–71, Detroit, Michigan, 1989. Also from Edinburgh as DAI Research Paper No. 504.Google Scholar

Copyright information

© Springer-Verlag Wien 1999

Authors and Affiliations

  • Somnuk Phon-Amnuaisuk
    • 1
  • Andrew Tuson
    • 1
  • Geraint Wiggins
    • 1
  1. 1.Music Informatics Research Group, Division of InformaticsUniversity of EdinburghEdinburghUK

Personalised recommendations