GenJam: evolution of a jazz improviser
Creative evolutionary systems
A mathematical theory of communication
ACM SIGMOBILE Mobile Computing and Communications Review
Automatic composition of music by means of grammatical evolution
APL '02 Proceedings of the 2002 conference on APL: array processing languages: lore, problems, and applications
A corpus-based hybrid approach to music analysis and composition
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Evolving computer-generated music by means of the normalized compression distance
SMO'05 Proceedings of the 5th WSEAS international conference on Simulation, modelling and optimization
Evolving four-part harmony using genetic algorithms
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part II
The geometry of musical rhythm
JCDCG'04 Proceedings of the 2004 Japanese conference on Discrete and Computational Geometry
Growing music: musical interpretations of l-systems
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
EvoMUSART'12 Proceedings of the First international conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design
Intelligent Generation of Rhythmic Sequences Using Finite L-systems
IIH-MSP '12 Proceedings of the 2012 Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing
evoDrummer: deriving rhythmic patterns through interactive genetic algorithms
EvoMUSART'13 Proceedings of the Second international conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design
Evolving structures for electronic dance music
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Music composition with algorithms inspired by nature has led to the creation of systems that compose music with rich characteristics. Nevertheless, the complexity imposed by unsupervised algorithms may arguably be considered as undesired, especially when considering the composition of rhythms. This work examines the composition of rhythms through L and Finite L-systems (FL-systems) and presents an interpretation from grammatical to rhythmic entities that expresses the repetitiveness and diversity of the output of these systems. Furthermore, we utilize a supervised training scheme that uses Genetic Algorithms (GA) to evolve the rules of L and FL-systems, so that they may compose rhythms with certain characteristics. Simple rhythmic indicators are introduced that describe the density, pauses, self similarity, symmetry and syncopation of rhythms. With fitness evaluations based on these indicators we assess the performance of L and FL-systems and present results that indicate the superiority of the FL-system in terms of adaptability to certain rhythmic tasks.