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Design and Application of an Improved Genetic Algorithm to a Class Scheduling System

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dc.contributor.author Chen, Xiangliu
dc.contributor.author Yue, Xiao-Guang
dc.contributor.author Li, Rita Yi Man
dc.contributor.author Zhumadillayeva, Ainur
dc.contributor.author Liu, Ruru
dc.date.accessioned 2024-12-09T07:20:08Z
dc.date.available 2024-12-09T07:20:08Z
dc.date.issued 2021
dc.identifier.citation Chen, X., Yue, X.-G., Li, R. Y. M., 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(01), pp. 44–59. https://doi.org/10.3991/ijet.v16i01.18225 ru
dc.identifier.issn 1863-0383
dc.identifier.other doi.org/10.3991/ijet.v16i01.18225
dc.identifier.uri http://rep.enu.kz/handle/enu/19974
dc.description.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. ru
dc.language.iso en ru
dc.publisher International Journal of Emerging Technologies in Learning (iJET) ru
dc.relation.ispartofseries 16(01), pp. 44–59;
dc.subject Course scheduling system ru
dc.subject Accuracy ru
dc.subject Running speed ru
dc.subject Computer algorithm ru
dc.title Design and Application of an Improved Genetic Algorithm to a Class Scheduling System ru
dc.type Article ru


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