Genetic Operators in Evolutionary Music Composition

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Sulyok:2018:SYNASC,

• author = "Csaba Sulyok",
• title = "Genetic Operators in Evolutionary Music Composition",
• booktitle = "2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)",
• year = "2018",
• pages = "253--259",
• month = sep,
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/SYNASC.2018.00047",
• abstract = "Genetic operators represent the alterations applied to entities within an evolutionary algorithm; they help create a new generation from an existing one, ensuring genetic diversity while also preserving the emergent overall strengths of a population. In this paper, we investigate different approaches to hyperparameter configuration of genetic operators within a linear genetic programming framework. We analyze the benefits of adaptively setting operator distributions and rates using hill climbing. A comparison is drawn between the constant and adaptive methodologies. This research is part of our ongoing work on evolutionary music composition, where we cast the actions of a virtual composer as instructions on a Turing-complete virtual register machine. The created music is assessed by statistical similarity to a given corpus. The frailty to change of our genotype dictates fine-tuning of the genetic operators to help convergence. Our results show that adaptive methods only provide a marginal improvement over constant settings and only in select cases, such as globally altering operator hyperparameters without changing the distribution. In other cases, they prove detrimental to the final grades.",
• notes = "Also known as \cite{8750692}",

}

Genetic Programming entries for Csaba Sulyok

Citations