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Optimizing preference satisfaction with genetic algorithm in matching students to supervisors |
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PP: 133-138 |
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doi:10.18576/amis/180114
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Author(s) |
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Azamat Serek,
Meirambek Zhaparov,
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Abstract |
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| The allocation of students to supervisors is a crucial aspect of higher education, impacting the quality of guidance and support students receive for their academic projects. This paper explores the application of a genetic algorithm to optimize the matching process. The algorithm considers considers psychological compatibility between student and supervisor, and aims for maximization of preference satisfaction of students and supervisors regarding the match. Experimental results demonstrate high preference satisfaction (0.91), indicating effective alignment with students’ preferences. The algorithm’s time and space complexities show scalability, making it a promising solution for large-scale applications. Additionally, the workload distribution results highlight the algorithm’s ability to balance the student load among supervisors.
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