A guided search genetic algorithm for the university course timetabling problem.

The university course timetabling problem is a combinatorial optimisation prob- lem in which a set of events has to be scheduled in time slots and located in suitable rooms. The design of course timetables for academic institutions is a very difficult task because it is an NP-hard problem. This paper proposes a genetic algorithm with a guided search strat- egy and a local search technique for the university course timetabling problem. The guided search strategy is used to create offspring into the population based on a data structure that stores information extracted from previous good individuals. The local search technique is used to improve the quality of individuals. The proposed genetic algorithm is tested on a set of benchmark problems in comparison with a set of state-of-the-art methods from the liter- ature. The experimental results show that the proposed genetic algorithm is able to produce promising results for the university course timetabling problem.

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