Optimizing preference satisfaction with genetic algorithm in matching students to supervisors

Authors

Azamat Serek, Department of Information Systems, SDU University, Kaskelen, KazakhstanFollow
Meirambek Zhaparov, Department of Computer Science, Paragon International University, Phnom Penh, CambodiaFollow

Author Country (or Countries)

Kazakhstan

Abstract

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.

Digital Object Identifier (DOI)

https://dx.doi.org/10.18576/amis/180114

Recommended Citation

Serek, Azamat and Zhaparov, Meirambek (2024) "Optimizing preference satisfaction with genetic algorithm in matching students to supervisors," Applied Mathematics & Information Sciences: Vol. 18: Iss. 1, Article 13.
DOI: https://dx.doi.org/10.18576/amis/180114
Available at: https://digitalcommons.aaru.edu.jo/amis/vol18/iss1/13