Music Composition Using Combination of Genetic Algorithms and Recurrent Neural Networks

Creativity has a fundamental role in music composition. One of the theories, which exist about creativity, is combination-theory. In this paper the suitability of genetic algorithms and recurrent neural networks for modeling this theory is considered. We discuss that two phases of combination occurs: one at the genetic algorithm level, and the other at the network level. One important challenge in automatic composition is the musical fitness. We trained are current neural network on two pieces of music to learn the coarse and fine statistical regularities to define a fitness function. In our implementation, pitch and duration properties of notes are considered to generate new melodic passages. The paper concludes that this method can produce music with novel combinations of pre-existing ideas that sound faithful to the learned music.