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Music Generation (MG) is an interesting research topic that links the art ofmusic and Artificial Intelligence (AI). The goal is to train an artificialcomposer to generate infinite, fresh, and pleasurable musical pieces. Music hasdifferent parts such as melody, harmony, and rhythm. In this paper, we proposea Generative Genetic Algorithm (GGA) to produce a melody automatically. Themain GGA uses a Long Short-Term Memory (LSTM) recurrent neural network as theobjective function, which should be trained by a spectrum of bad-to-goodmelodies. These melodies have to be provided by another GGA with a differentobjective function. Good melodies have been provided by CAMPINs collection. Wehave considered the rhythm in this work, too. The experimental results clearlyshow that the proposed GGA method is able to generate eligible melodies withnatural transitions and without rhythm error.

GGA-MG: Generative Genetic Algorithm for Music Generation