Design and Application of an Improved Genetic Algorithm to a Class Scheduling System
Xiangliu Chen, Guilin University of Electronic Technology ; Xiao-Guang Yue, European University Cyprus ; Rita Li, Hong Kong Shue Yan University ; Ainur Zhumadillayeva, L.N.Gumilyov Eurasian National UniversityFaculty of Information Technologies, ; Ruru Liu, Chizhou University
iJET Volume 16, Number 1, ISSN 1863-0383 Publisher: International Journal of Emerging Technology in Learning, Kassel, Germany
The current expansion of national colleges and universities or the increase in the number of enrolments requires teaching management to ensure the quality of teaching. The problem of scheduling is a very complicated prob-lem in teaching management, and there are many restrictions. If the number of courses scheduled is large, it will be necessary to repeat the experiment and make adjustments. This kind of work is difficult to accomplish accu-rately by manpower. Moreover, for a comprehensive university, there are many subjects, many professional settings, limited classroom resources, limited multimedia classroom resources, and other factors that limit and constrain the results of class scheduling. Such a large data volume and com-plicated workforce are difficult to complete accurately. Therefore, manpow-er scheduling cannot meet the needs of the educational administration of colleges and universities. Today, computer technology is highly developed. It is very economical to use software technology to design a course schedul-ing system and let the computer complete this demanding and rigorous work. Common course scheduling systems mainly include hill climbing al-gorithms, tabu search algorithms, ant colony algorithms, and simulated an-nealing algorithms. These algorithms have certain shortcomings. In this re-search, we investigated the mutation genetic algorithm and applied the algo-rithm to the student’s scheduling system. Finally, we tested the running speed and accuracy of the system. We found that the algorithm worked well in the course scheduling system and provided strong support for solving the tedious scheduling work of the educational administration staff.
Chen, X., Yue, X.G., Li, R., Zhumadillayeva, A. & Liu, R. (2021). Design and Application of an Improved Genetic Algorithm to a Class Scheduling System. International Journal of Emerging Technologies in Learning (iJET), 16(1), 44-59. Kassel, Germany: International Journal of Emerging Technology in Learning.