| Summary: | 碩士 === 國立中正大學 === 資訊工程研究所 === 103 === Automatic music generation is an interesting issue in computer science. During the past few decades, numerous papers were discussed this issue, but most of them focused on monophonic music at the present stage. This thesis studies the problem of Automatic generation of Polyphonic music, which allows plurality of notes existing in a time slot. We adopt pieces of music generated by Recurrent Neural Networks - Restricted Boltzmann Machine (RNN-RBM) and take these licks to serve as an initial population in our genetic system. The fitness function of our system basically employs music theory, i.e. hardwired fitness functions. After evolution, we adjust some piece to fit Ternary form (a kind of music form). In the end of thesis, we made a comparison between different fitness and dataset.
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