abstract

Melody composition using geometric crossover for variable-length encoding

Authors:

Yong-Wook Nam,

Yong-Hyuk KimAuthors Info & Claims

GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 37 - 38

https://doi.org/10.1145/3067695.3082041

Published: 15 July 2017 Publication History

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Abstract

This paper proposes a system that uses interactive genetic algorithm (IGA) to automatically compose songs according to the userfis preferences. As notes have duration and pitch, the number of notes within a measure is subject to change. Geometric crossover can be used to make offspring notes while preserving the positive characteristics within such variable-length sequences. The proposed automatic composition system uses geometric crossover in IGA to produce songs customized to the user. Experimental results indicate that the proposed system effectively generates new notes from variable-length note sequences within a single measure.

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Cited By

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Mycka JMańdziuk J(2024)Artificial intelligence in music: recent trends and challengesNeural Computing and Applications10.1007/s00521-024-10555-xOnline publication date: 16-Nov-2024
https://doi.org/10.1007/s00521-024-10555-x
Nam YKim Y(2019)Automatic Jazz Melody Composition Through a Learning-Based Genetic AlgorithmComputational Intelligence in Music, Sound, Art and Design10.1007/978-3-030-16667-0_15(217-233)Online publication date: 27-Mar-2019
https://doi.org/10.1007/978-3-030-16667-0_15
Nam YKim YTakadama K(2018)A geometric evolutionary search for melody compositionProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3208768(53-54)Online publication date: 6-Jul-2018
https://dl.acm.org/doi/10.1145/3205651.3208768

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Published In

GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2017

1934 pages

ISBN:9781450349390

DOI:10.1145/3067695

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 July 2017

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Funding Sources

National Research Foundation of Korea (NRF) by the Ministry of Education

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GECCO '17

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SIGEVO

GECCO '17: Genetic and Evolutionary Computation Conference

July 15 - 19, 2017

Berlin, Germany

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

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Mycka JMańdziuk J(2024)Artificial intelligence in music: recent trends and challengesNeural Computing and Applications10.1007/s00521-024-10555-xOnline publication date: 16-Nov-2024
https://doi.org/10.1007/s00521-024-10555-x
Nam YKim Y(2019)Automatic Jazz Melody Composition Through a Learning-Based Genetic AlgorithmComputational Intelligence in Music, Sound, Art and Design10.1007/978-3-030-16667-0_15(217-233)Online publication date: 27-Mar-2019
https://doi.org/10.1007/978-3-030-16667-0_15
Nam YKim YTakadama K(2018)A geometric evolutionary search for melody compositionProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3208768(53-54)Online publication date: 6-Jul-2018
https://dl.acm.org/doi/10.1145/3205651.3208768

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A geometric evolutionary search for melody composition

Automatic Jazz Melody Composition Through a Learning-Based Genetic Algorithm

A new crossover mechanism for genetic algorithms with variable-length chromosomes for path optimization problems