Hybrid genetic algorithm for university examination timetabling problem

This paper considers a Hybrid Genetic Algorithm (HGA) for University Examination Timetabling Problem (UETP). UETP is defined as the assignment of a given number of exams and their candidates to a number of available timeslots while satisfying a given set of constraints. Solutions for uncapacitated UETP are presented where five domain-specific knowledge in the form of low-level heuristics are used to guide the construction of the timetable in the initial population. The main components of the genetic operators in a GA will be tested and the best combination of the genetic operators will be adopted to construct a Pure Genetic Algorithm (PGA). The PGA will then hybridised with three new local optimisation techniques, which will make up the HGA; to improve the solutions found. These new local optimisation techniques will arrange the timeslots and exams using new explicit equations, if and only if, the modification will reduce the penalty cost function. The performance of the proposed HGA is compared with other metaheuristics from literature using the Carter’s benchmark dataset which comprises of real-world timetabling problem from various universities. The computational results show that the proposed HGA outperformed some of the metaheuristic approaches and is comparable to most of the well-known metaheuristic approaches.

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