Evolutionary Music Composition

Dostál, Martin

doi:10.1007/978-3-642-30504-7_37

Cited by 11 publications

(2 citation statements)

References 10 publications

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“…Genetic algorithms are suitable for the automation of incremental exploration and selection in multiple ways. Burton and Vladimirova [19] have studied several genetic methods applied to the generation of musical sequences; Dostál [20] also published a survey of techniques of evolutionary composition. GenoMus design favors genetic search processes in a very flexible manner, since data structures have no determined length and are one-dimensional.…”

Section: Knowledge Representation Optimized For Machine Learning By D...mentioning

confidence: 99%

GenoMus: Representing Procedural Musical Structures with an Encoded Functional Grammar Optimized for Metaprogramming and Machine Learning

2022

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We present GenoMus, a new model for artificial musical creativity based on a procedural approach, able to represent compositional techniques behind a musical score. This model aims to build a framework for automatic creativity, that is easily adaptable to other domains beyond music. The core of GenoMus is a functional grammar designed to cover a wide range of styles, integrating traditional and contemporary composing techniques. In its encoded form, both composing methods and music scores are represented as one-dimensional arrays of normalized values. On the other hand, the decoded form of GenoMus grammar is human-readable, allowing for manual editing and the implementation of user-defined processes. Musical procedures (genotypes) are functional trees, able to generate musical scores (phenotypes). Each subprocess uses the same generic functional structure, regardless of the time scale, polyphonic structure, or traditional or algorithmic process being employed. Some works produced with the algorithm have been already published. This highly homogeneous and modular approach simplifies metaprogramming and maximizes search space. Its abstract and compact representation of musical knowledge as pure numeric arrays is optimized for the application of different machine learning paradigms.

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Section: Knowledge Representation Optimized For Machine Learning By D...mentioning

confidence: 99%

GenoMus: Representing Procedural Musical Structures with an Encoded Functional Grammar Optimized for Metaprogramming and Machine Learning

2022

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“…Evolutionary computing techniques have been applied in a large variety of different settings ranging from classical optimization problems in the context of supply chain management and renewable energy [BM16, TWD + 13, NAW20] over to the creation of music and art [Dos13,Lew08,NAN20]. The easy applicability of evolutionary algorithms makes them attractive also to users from outside computer science disciplines and is one of the major reason for their success in a wide range of engineering applications such as the design of water networks [BDM15] or processing and planning in mining [MD10,OWBM13].…”

Section: Introductionmentioning

confidence: 99%

A Survey on Recent Progress in the Theory of Evolutionary Algorithms for Discrete Optimization

Doerr,

Neumann

2020

Preprint

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The theory of evolutionary computation for discrete search spaces has made a lot of progress during the last ten years. This survey summarizes some of the most important recent results obtained in this research area. It reviews important methods such as drift analysis, discusses theoretical insight on parameter tuning and parameter control, and summarizes the advances made for stochastic and dynamic problems. Furthermore, the survey highlights important results in the area of combinatorial optimization with a focus on parameterized complexity and the optimization of submodular functions. Finally, it gives an overview on the large amount of new important results for estimation of distribution algorithms.

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An Evolutionary Approach to Generate Rhythm and Melody Based on Repertories

Jeong

Ahn

2015

Communications in Computer and Information Science

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Evolutionary Music Composition

Cited by 11 publications

References 10 publications

GenoMus: Representing Procedural Musical Structures with an Encoded Functional Grammar Optimized for Metaprogramming and Machine Learning

GenoMus: Representing Procedural Musical Structures with an Encoded Functional Grammar Optimized for Metaprogramming and Machine Learning

A Survey on Recent Progress in the Theory of Evolutionary Algorithms for Discrete Optimization

An Evolutionary Approach to Generate Rhythm and Melody Based on Repertories

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