A Multi-task Combinatorial Optimization Model Based on Genetic Algorithm and its Application in College Education Curriculum Planning

Jianying Li, Zhe Zhou, Liying Wang


Multi-task combinatorial optimization of a complex system is an important aspect of multi-task planning. To address the existing defects and limitations of the existing multi-task combinatorial optimization methods, the paper proposes a multi-task combinatorial model based on genetic algorithm. As a complex multi-task combinatorial optimization, the curriculum planning for higher education applies to itself the multi-task combinatorial model, which is based on genetic algorithm. Having fully considered such factors as teaching resources distribution, students’ intention and teachers’ intention, the paper designs a more efficient fitness function that has flexibly distributed courses and time in curriculum planning to meet the need of teaching in higher schools. Meanwhile, the paper utilizes specific cases of higher education to verify and analyze the algoritnm, and also carryies out a simulation test under the Matlab environment.
The result indicates that the multi-task combinatorial optimization model based on genetic algorithm can relatively significantly optimize curriculum planning of higher education.


College education; Curriculum planning; Fuzzy combinatorial optimization model; Genetic algorithm; Multi task

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Copyright (c) 2017 Jianying Li, Zhe Zhou, Liying Wang

International Journal of Emerging Technologies in Learning (iJET) – eISSN: 1863-0383
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