Tags:adaptive operator, algorithmic music composition, evolutionary algorithms, evolutionary music composition, genetic operators, genetic programming, linear genetic programming and virtual composer
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.
Genetic Operators in Evolutionary Music Composition