Hybridizing Genetic Algorithm and Record-to-Record Travel Algorithm for Solving Uncapacitated Examination Timetabling Problem
AbstractExamination timetabling is one of the most important administrative tasks in academic institutions. They are used to schedule examinations into timeslots and rooms. Many methods have been developed to solve examination timetabling problems. Metaheuristics have shown good results especially if they are hybridized with other methods. Genetic Algorithms (GAs) are one of the techniques that have been used in optimization problems. Record-to-Record Travel (RRT) is another optimization method that has been introduced for local search. In this paper, weÂ describe the combined use of GA and RRT, called GARRT. In particular, the process of hybridization of the two algorithms to solve the uncapacitated examination timetabling problems is discussed. GARRT aims to balance the global search (by GA) and the local search (by RRT). This work uses Carterâ€™s benchmark datasets as the testbed. Simulation results showed that GARRT performed better when compared to the results generated by GA approach alone. A good result is achieved by minimizing the violation of the soft constraints.
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