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Hill Climbing versus Genetic Algorithm Optimization in Solving the Examination Timetabling Problem

In this paper, we compare the incorporation of Hill Climbing (HC) and Genetic Algorithm (GA) optimization in our proposed methodology in solving the examination scheduling problem. It is shown that our greedy HC optimization outperforms the GA in all cases when tested on the benchmark datasets. In our implementation, HC consumes more time to execute compared to GA which manages to improve the quality of the initial schedules in a very fast and efficient time. Despite this, since the amount of time taken by HC in producing improved schedules is considered reasonable and it never fails to produce better results, it is suggested that we incorporate the Hill Climbing optimization rather than GA in our work.