- author = "Csaba Sulyok and Christopher Harte and Zalan Bodo",
- title = "On the impact of domain-specific knowledge in evolutionary music composition",
- booktitle = "GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference",
- year = "2019",
- editor = "Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie {Peprez Caceres} and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre",
- isbn13 = "978-1-4503-6111-8",
- pages = "188--197",
- address = "Prague, Czech Republic",
-
DOI = "
doi:10.1145/3321707.3321710",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- month = "13-17 " # jul,
- organisation = "SIGEVO",
- keywords = "genetic algorithms, genetic programming, evolutionary music composition, domain-specific languages",
- size = "10 pages",
-
abstract = "In this paper we investigate the effect of embedding
different levels of musical knowledge in the virtual
machine (VM) architectures and phenotype
representations of an algorithmic music composition
system. We examine two separate instruction sets for a
linear genetic programming framework that differ in
their knowledge of musical structure: one a
Turing-complete register machine, unaware of the nature
of its output; the other a domain-specific language
tailored to operations typically employed in the
composition process. Our phenotype, the output of the
VM, is rendered as a musical model comprising a
sequence of notes represented by duration and pitch. We
compare three different pitch schemes with differing
embedded knowledge of tonal concepts, such as key and
mode.
To derive a fitness metric, we extract musical features from a corpus of Hungarian folk songs in the form of n-grams and entropy values. Fitness is assessed by extracting those same attributes from the phenotype and finding the maximal similarity with representative corpus features.
With two different VM architectures and three pitch schemes, we present and compare results from a total of six configurations, and analyse whether the domain-specific knowledge impacts the results and the rate of convergence in a beneficial manner.",
- notes = "Also known as \cite{3321710} GECCO-2019 A Recombination of the 28th International Conference on Genetic Algorithms (ICGA) and the 24th Annual Genetic Programming Conference (GP)",
