Optimizing preference satisfaction with genetic algorithm in matching students to supervisors
Serek A. Zhaparov M.
2024Natural Sciences Publishing
Applied Mathematics and Information Sciences
2024#18Issue 1133 - 138 pp.
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.
genetic algorithm , higher education , preference satisfaction , psychological compatibility , student-supervisor matching
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Department of Information Systems, SDU University, Kaskelen, Kazakhstan
Department of Computer Science, Paragon International University, Phnom Penh, Cambodia
Department of Information Systems
Department of Computer Science
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026