MU_PSYC: Music Psychology Enriched Genetic Algorithm

Recent advancement of artificial intelligence (AI) techniques have impacted the field of algorithmic music composition, and that has been evidenced by concert performances wherein the audience reportedly often could not tell whether music was composed by machine or by human. Among the AI techniques, genetic algorithms dominate the field due to their suitability for both creativity and optimization. Recently, many attempts have been made to incorporate rules from traditional music theory to design and automate genetic algorithms. However, due to the exclusive use of a very constrained set of traditional music rules and their primitive nature, the field seems to face stagnation. This paper is aimed at addressing the above limitation and hence paving the way to advance the field towards composing human-quality music. The basic idea is to look beyond this constrained set of traditional music rules towards music universals and music-psychology. To demonstrate the proposed approach, we implemented a prototype genetic algorithm and conducted a limited experiment to test the quality of its composition. The initial result is interesting and encouraging.