A Comparison between Genetic and Memetic Algorithm for Automated Music Composition System

Automatic music composition has been a challenging, interesting, and yet still much to be explored task primarily because it is hard to distinguish which song is good or bad which significantly impedes the automated composition process. Despite this difficulty, automated music composition would benefit many groups of people who ought to use a piece of their own music, as composed for them by an AI system with compositional intelligence, without someone else’s copyright for some purpose such as a music piece for a commercial, or a song played in the background of a presentation. Our composition system composes eight-bar tracks, based on western music theory and listener evaluation. We present here the use of memetic algorithm, comparing to using the conventional genetic algorithm. The same representation and evaluation for both techniques are used because of the similarity of these two algorithms. The main difference of memetic algorithm with genetic algorithm is the local search process. Both algorithms are implemented separately to spot the difference between the results then we evaluated the algorithms. When the outcomes are compared, we found that the use of memetic algorithm performs better in terms of quality of musical piece and convergence speed.