Generating Rhythms with Genetic Algorithms

Abstract

Interactive genetic algorithms (IGAs [Smith 91]) are explored as a mechanism for controlling music generation systems. IG As are well-suited for this task because they allow a user to simply choose which musical material he likes, without necessarily understanding the details or parameters of how the system is generating this material. Specifically, my system uses an IGA to learn a user's criteria for generating percussion textures. As the system learns (develops an increasingly accurate model of the function which represents the user's criteria), the quality of the rhythms it produces improves to suit the user's taste. This paper describes two version of the system: 1) the initial version which demonstrates the -efficacy of the G A to produce near-optimal desired rhythmic figures in a simplified rhythmic domain; 2) the current version which is designed to be of practical use in evolving complex rhythmic textures. This approach is largely inspired by the work of Richard Dawkins, who succinctly summarizes the attraction of IGAs for artistic endeavors: "Effective searching procedures become, when the search space is sufficiently large, indistinguishable from true creativity" [Dawkins 86].