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Paving the Way Towards Evolutionary Machine Teaching: An Application to 4-Part Harmony

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Elia Pacioni
Francisco Fernández De Vega
Published in
Artificial Evolution
Publisher
Springer Nature Switzerland

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Abstract

This chapter delves into the application of evolutionary algorithms and machine teaching to enhance the composition of 4-part harmony in music. It explores how student learning processes can be leveraged to optimize AI-driven music composition, focusing on the SATB (Soprano, Alto, Tenor, Bass) technique. The text analyzes the search space of possible chord pairs and how students naturally narrow this space through their learning, leading to more efficient and higher-quality solutions. The study involves collecting and analyzing over 13,000 student exercises to identify patterns and reduce the solution space, resulting in a significant improvement in the quality and speed of AI-generated musical scores. The findings demonstrate the potential of integrating human teaching methods with evolutionary algorithms to tackle complex real-world problems in music composition.
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previous chapter A Study of an Ant-Based Algorithm to Model Cyclist Behavior in Bicycle-Friendly Cities
next chapter Scalable Local Optima Networks for Continuous Search Spaces
Title
Paving the Way Towards Evolutionary Machine Teaching: An Application to 4-Part Harmony
Authors
Elia Pacioni
Francisco Fernández De Vega
Copyright Year
2026
Book
Artificial Evolution

Print ISBN: 978-3-032-07997-8

Electronic ISBN: 978-3-032-07998-5

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-3-032-07998-5_5
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