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Title: Design and application of an improved genetic algorithm to a class scheduling system
Authors: Chen, Xiangliu 
Yue, Xiao-Guang 
Dr. LI Yi Man, Rita 
Zhumadillayeva, Ainur 
Liu, Ruru 
Issue Date: 2020
Source: International Journal of Emerging Technology in Learning, 2020, vol. 16(1), pp. 44-59.
Journal: International Journal of Emerging Technology in Learning 
Abstract: 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 problem 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 accurately 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 complicated workforce are difficult to complete accurately. Therefore, manpower 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 scheduling system and let the computer complete this demanding and rigorous work. Common course scheduling systems mainly include hill climbing algorithms, tabu search algorithms, ant colony algorithms, and simulated annealing algorithms. These algorithms have certain shortcomings. In this research, we investigated the mutation genetic algorithm and applied the algorithm 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.
Description: Open access
Type: Peer Reviewed Journal Article
ISSN: 1868-8799
DOI: 10.3991/IJET.V16I01.18225
Appears in Collections:Economics and Finance - Publication

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