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Developing Fitness Functions for Pleasant Music: Zipf’s Law and Interactive Evolution Systems

  • Bill Manaris
  • Penousal Machado
  • Clayton McCauley
  • Juan Romero
  • Dwight Krehbiel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3449)

Abstract

In domains such as music and visual art, where the quality of an individual often depends on subjective or hard to express concepts, the automating fitness assignment becomes a difficult problem. This paper discusses the application of Zipf’s Law in evaluation of music pleasantness. Preliminary results indicate that a set of Zipf-based metrics can be effectively used to classify music according to pleasantness as reported by human subjects. These studies suggest that metrics based on Zipf’s law may capture essential aspects of proportion in music as it relates to music aesthetics. We discuss the significance of these results for the automation of fitness assignment in evolutionary music systems.

Keywords

Average Success Rate Style Identification Interactive Evolution Fitness Assignment Music Excerpt 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Bill Manaris
    • 1
  • Penousal Machado
    • 2
  • Clayton McCauley
    • 1
  • Juan Romero
    • 3
  • Dwight Krehbiel
    • 4
  1. 1.Computer Science DepartmentCollege of CharlestonCharlestonUSA
  2. 2.Instituto Superior de Engenharia de CoimbraCoimbraPortugal
  3. 3.Creative Computer Group – RNASA Lab – Faculty of Computer ScienceUniversity of A CoruñaCoruñaSpain
  4. 4.Psychology DepartmentBethel CollegeNorth NewtonUSA

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