초록

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Abstract ▼ AI-Helper

This paper proposes an automatic making of an optimal lecture timetable that minimizes an penalty function under the various constraints using genetic algorithms. The constraints are classified into both hard and soft constraint and penalty values are differently given according to their viciousnes...

This paper proposes an automatic making of an optimal lecture timetable that minimizes an penalty function under the various constraints using genetic algorithms. The constraints are classified into both hard and soft constraint and penalty values are differently given according to their viciousness on the penalty function when they are violated. A chromosome in the population is represented by a sequence of lectures and any timeslot can be assigned to each lecture. The iteration time is greatly reduced by predetermining the possible timeslots of each lecture from the pre-known informations and by applying graph coloring technique to the timeslot assignment in a way that the lecture having smaller number of possible timeslots is assigned first. The timetable population has been genetically evolved such that the timeslot assignment will decrease the penalty function by applying both the k-tournament selection combined with the elitism and the incremental hybrid crossover operation. The proposed method is performed to determine the lecture timetable of spring semester, 1995 in our department and compares various conflict performances with the hand-coded one.