The art of music, integral to the human experience, has been present since ancient civilizations. Diverse musical styles emerge from various cultures, geographic locations, and historical periods. Nevertheless, creative individuals may unknowingly produce compositions resembling existing works, even without exposure to similar pieces. Consequently, the cultivation of evolutionary music is crucial for generating a broad spectrum of compositions less likely to evoke familiarity. Hence, music appreciation is significantly subjective from one person to another in society. With the advancement of today’s technology, evolutionary music can be created at ease using algorithmic composition methods from evolutionary algorithms. In this manuscript, we present a method for creating concise, non-binary genetic representations of music notes to generate short, monophonic melodies without harmonic accompaniment. This approach is specifically tailored for the preliminary stage of the research project. Genetic Algorithm (GA) is a metaheuristic about an evolutionary algorithm based on the natural selection processes, which are appropriate for the approach. Genetic Algorithms are versatile that can be combined with other algorithms to produce melodies, harmonies, chords, music structures, scales and others. The novelty of my proposed method is able to create monophony melodies with just solely genetic algorithm without having additional algorithms for the rhythms. Therefore, a literature survey is conducted to provide insight into the latest research using other machine learning methods for evolutionary music. Deep learning is a subset newer to machine learning that increases the accuracy of the results with more extensive data sets.