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Progress in Industrial Mathematics at ECMI 2000 pp 126–139Cite as

Evolutionary Music and Fitness Functions

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Part of the book series: Mathematics in Industry ((TECMI,volume 1))

Abstract

Is it possible to obtain pleasant evolved music by means of a fitness function, without human intervention? In this work a method based on a genetic algorithm to produce automatic music is presented. In particular we developed a fitness function based on consonance, which allows to evaluate the “pleasantness” of a sequence of notes generated by an algorithm. The fitness function has then been used within genetic algorithms to help the resulting melodies evolve. This function has been used with cellular automata. An initial sequence will allowed to evolve within a space-time pattern and then turned into music as is suitable. The use of the Fitness function permits the search for and the choosing of appropriate rules, which generate pleasant melodic sequences. The best results are obtained for CA whose state varies between 0 and 3 and for small lattice.

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Author information

Authors and Affiliations

  1. Centro Interdipartimentale della Comunicazione, Università della Calabria, Italy

    E. Bilotta, P. Pantano & V. Talarico

Authors
  1. E. Bilotta
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  2. P. Pantano
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  3. V. Talarico
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Catania, Viale A. Doria 6, 95125, Catania, Italy

    Angelo Marcello Anile

  2. MIRIAM and Department of Mathematics, University of Milano, Via C. Saldini 50, 20133, Milano, Italy

    Vincenzo Capasso

  3. Department of Mathematics and its Applications, University of Palermo, Via Archirafi 34, 90123, Palermo, Italy

    Antonio Greco

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© 2002 Springer-Verlag Berlin Heidelberg

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Bilotta, E., Pantano, P., Talarico, V. (2002). Evolutionary Music and Fitness Functions. In: Anile, A.M., Capasso, V., Greco, A. (eds) Progress in Industrial Mathematics at ECMI 2000. Mathematics in Industry, vol 1. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-04784-2_16

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  • DOI: https://doi.org/10.1007/978-3-662-04784-2_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-07647-3

  • Online ISBN: 978-3-662-04784-2

  • eBook Packages: Springer Book Archive

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