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Development of a University timetable automation using genetic algorithm
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Abstract Scheduling course timetables for a large array of courses is a very complex problem that often has to be solved manually by the center staff even though results are not always fully optimal. Timetabling being a highly constrained combinatorial problem, this work attempts to put into play the effectiveness of evolutionary techniques based on Darwin’s theories to solve the timetabling problem if not fully optimal but near-optimal. Genetic Algorithm is a popular meta-heuristic that has been successfully applied to many hard combinatorial optimization problems which includes timetabling and scheduling problems. In this work, the course sets, halls and time allocations are represented by a multidimensional array on which a local search is performed and a combination of the direct representation of the timetable with heuristic crossover is made to ensure that fundamental constraints are not violated. Finally, the genetic algorithm was applied in the development of a viable timetabling system which was tested to demonstrate the variety of possible timetables that can be generated based on user-specified constraints and requirements.