Performance Improvement of Genetic Algorithm Based Exam Seating Solution by Parameter Optimization

• Published in: Journal of innovative science and engineering Vol. 6; no. 2; pp. 220 - 232
• Main Authors: AĞALDAY, Fatih, NİZAM, Ali
• Format: Journal Article
• Language: English
• Published: Bursa Technical University 01.12.2022
• Subjects: data science applications in education; genetic algorithm; multiparameter optimization
• ISSN: 2602-4217; 2602-4217
• DOI: 10.38088/jise.1006070

Exam seat allocation becomes a complex problem concerning the increasing number of students, subjects, exams, departments, and rooms in a higher institution. The requirements and constraints of this problem demonstrate similar characteristics to extensively researched exam timetabling problems. They are planning limited capacity effectively and efficiently. Additionally, exam seating requires a seating arrangement to reduce cheating incidents. In the literature, several genetic algorithm-based methods were recommended to prevent students that have a friendship from sitting close during the exams while providing the best exam session arrangement. We have improved the performance of the genetic algorithm by parameter optimization and a new elitism method to increase saturation rate and accuracy. The algorithm has been tested on a real-world data set and represents a high potential for the realization of a high-quality seating arrangement compatible with the real world.