We propose an tabu search algorithm using an candidate list stratety with random sampling for the university course timetabling problem, where the neighborhood size can be adjusted by a parameter ratio. With this framework, we can control the trade-off between exploration and exploitation by adjusting the neighborhood size. Experimental results show that the proposed algorithm outperforms state-of-the-art algorithms when the neighborhood size is set properly.
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