In our ongoing work on evolutionary music composition, we explore linear genetic programming as a method of creating a virtual music composer. This process hinges on viewing the composer as a Turing-complete virtual register machine that outputs pieces of music. In this paper we compare different designs for the virtual machine, exploring various instruction sets and memory architectures; analysing their ability to create music statistically similar to that of a given corpus. We also explore different genotype sizes to see how much memory the virtual machine needs to converge to an acceptable result.

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© 2017 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license
2017
MIT Press
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