Evolutionary music: applying evolutionary computation to the art of creating music

Abstract

We present a review of the application of genetic programming (GP) and other variations of evolutionary computation (EC) to the creative art of music composition. Throughout the development of EC methods, since the early 1990s, a small number of researchers have considered aesthetic problems such as the act of composing music alongside other more traditional problem domains. Over the years, interest in these aesthetic or artistic domains has grown significantly. We review the implementation of GP and EC for music composition in terms of the compositional task undertaken, the algorithm used, the representation of the individuals and the fitness measure employed. In these aesthetic studies we note that there are more variations or generalisations in the algorithmic implementation in comparison to traditional GP experiments; even if GP is not explicitly stated, many studies use representations that are distinctly GP-like. We determine that there is no single compositional challenge and no single best evolutionary method with which to approach the act of music composition. We consider autonomous composition as a computationally creative act and investigate the suitability of EC methods to the search for creativity. We conclude that the exploratory nature of evolutionary methods are highly appropriate for a wide variety of compositional tasks and propose that the development and study of GP and EC methods on creative tasks such as music composition should be encouraged.