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.