An optimized solution to the course scheduling problem in universities under an improved genetic algorithm
Abstract: The increase in the size of universities has greatly increased the number of teachers, students, and courses and has also increased the difficulty of scheduling courses. This study used coevolution to improve the genetic algorithm and applied it to solve the course scheduling problem in universities. Finally, simulation experiments were conducted on the traditional and improved genetic algorithms in MATLAB software. The results showed that the improved genetic algorithm converged faster and produced better solutions than the traditional genetic algorithm under the same crossover and mutation probability. As the mutation probability in the algorithm increased, the fitness values of both genetic algorithms gradually decreased, and the computation time increased. With the increase in crossover probability in the algorithm, the fitness value of the two genetic algorithms increased first and then decreased, and the computational time decreased first and then increased.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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An optimized solution to the course scheduling problem in universities under an improved genetic algorithm ; volume:31 ; number:1 ; year:2022 ; pages:1065-1073 ; extent:9
Journal of intelligent systems ; 31, Heft 1 (2022), 1065-1073 (gesamt 9)
- Urheber
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Zhang, Qiang
- DOI
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10.1515/jisys-2022-0114
- URN
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urn:nbn:de:101:1-2022091614153965850834
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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15.08.2025, 07:21 MESZ
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Beteiligte
- Zhang, Qiang
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