Comparison of performance between different selection strategies on genetic algorithm with course timetabling problem

Course timetabling is an NP-hard problem. There are many factors to be considered. A GA is suitable for NP-hard and optimization problems and it can also be applied to various problems. Three main operators of GA are selection, crossover, and mutation. This paper compares performance on a GA when different selection strategies: roulette wheel selection, rank selection, and tournament selection, are applied. A good selection strategy tries to keep good solutions and leave the bad ones out of a population. The experimental result demonstrates that GA with roulette wheel selection works more efficient than the others for producing feasible course timetables.