Optimizing music course scheduling with real number encoding and chaos genetic algorithm

Abstract

The scheduling process of music courses in education is complex and difficult to optimize. Traditional scheduling systems usually use simple algorithms or manual intervention, resulting in low efficiency and uneven resource allocation. To optimize the resource allocation and course scheduling of music courses, considering the limitations of genetic algorithms, the randomness and traversal characteristics of introducing chaotic systems were studied to optimize population diversity, forming a new scheduling method based on chaotic genetic algorithms. This study used music course data from a particular school, including classroom resources, number of students, course time, etc. The results showed that after 300 iterations, the average running time of the research method decreased by 76.57 %, 66.46 %, 58.39 %, and 48.24 %, respectively. Meanwhile, this research method not only had the fastest convergence speed, but also had the highest fitness function value during the convergence process. In practical applications, this research method significantly improved students' music grades, demonstrating its effectiveness in optimizing the music course scheduling system. This study provides a new research direction for future educational scheduling systems.