Towards Melodic Extension Using Genetic Algorithms

Genetic Algorithms (GA's) are considered promising for music composition because they combine ‘creativity’ (ability to explore a large search space) with constraints (creative 'excess' is 'pruned' using a fitness function). A major difficulty with the use of GA's for this task is to define fitness functions which capture the aesthetic qualities of the wide range of successful melodies. In this paper we report on research that addresses this problem in the context of a modest compositional task, melodic extension. We describe 21 melodic features used as the basis for a GA fitness function and for mutation procedures. We discuss how the features were chosen, measured for significance, and might be incorporated into a fitness function.