Evolutionary Music Composition Using Tokenized Representation Based on Byte Pair Encoding

Natural language processing (NLP) research has achieved remarkable advancement in recent decades. Language data and music data share several common features, enabling NLP techniques to be applied to music-related tasks, including music generation. One of the fundamental techniques in NLP is tokenization, which is designed to create more efficient representations of text. Inspired by the shared nature of language and music, this study proposes using tokenization algorithms to design a high-level representation that facilitates evolutionary music composition. Traditional evolutionary music composition systems typically employ an integer string to represent a melody. In contrast, the tokenized representation can encode more complex melodic information with a shorter chromosome than integer strings. A series of experiments were conducted to evaluate the effectiveness and efficiency of the proposed representation. The results demonstrate a significant improvement in both the expressiveness and efficiency of melody composition.