  Music generation with the aid of computers has been recently grabbed the
attention of many scientists in the area of artificial intelligence. Deep
learning techniques have evolved sequence production methods for this purpose.
Yet, a challenging problem is how to evaluate generated music by a machine. In
this paper, a methodology has been developed based upon an interactive
evolutionary optimization method, with which the scoring of the generated
melodies is primarily performed by human expertise, during the training. This
music quality scoring is modeled using a Bi-LSTM recurrent neural network.
Moreover, the innovative generated melody through a Genetic algorithm will then
be evaluated using this Bi-LSTM network. The results of this mechanism clearly
show that the proposed method is able to create pleasurable melodies with
desired styles and pieces. This method is also quite fast, compared to the
state-of-the-art data-oriented evolutionary systems.
