Exam Seating Allocation to Prevent Malpractice Using Genetic Multi-optimization Algorithm

• Published in: Machine Learning and Metaheuristics Algorithms, and Applications Vol. 1366; pp. 131 - 145
• Main Authors: Kashyap, Madhav M., Thejas, S., Gaurav, C. G., Srinivas, K. S.
• Format: Book Chapter
• Language: English
• Published: Singapore Springer 2021; Springer Singapore
• Series: Communications in Computer and Information Science
• Subjects: Arrangement; Combinatorics; Evolutionary computing; Genetic algorithm; Metaheuristic; NP complete; NP hard; Permutation; Seating
• ISBN: 9811604185; 9789811604188
• ISSN: 1865-0929; 1865-0937
• DOI: 10.1007/978-981-16-0419-5_11

Despite unceasing debate about it’s pros and cons, exams and standardized testing have emerged as the main mode of evaluation and comparison in our increasingly competitive world. Inevitably, some examinees attempt to illegally gain an unfair advantage over other candidates by indulging in cheating and malpractice. Even a single case of examination malpractice can destroy an Examination body’s credibility and even lead to costly and time-consuming legal proceedings. Our paper attempts to strategically allot examinees in specific seats and rooms, such as to mitigate the overall probability of malpractice. It involves examining multiple crucial factors such as subject similarity, distancing between examinees, and human field of vision to find the most optimal seating arrangement. We have exploited the property of Evolutionary Genetic Algorithms to find globally optimal or close to optimal solutions in an efficient time for this otherwise NP-complete permutation problem.