Using a genetic algorithm optimizer tool to solve University timetable scheduling problem

University course timetabling is a NP-hard problem which is very difficult to solve by conventional methods. A highly constrained combinatorial problem, like the timetable, can be solved by evolutionary methods. In this paper, among the evolutionary computation (EC) algorithms, a genetic algorithm (GA) for solving university course timetabling problems is applied. Main goal is to minimize the number of conflicts in the timetable. For this purpose two approaches - modified GA and cooperative GA - are applied. Results show the modified GA (MGA) method was significantly enhanced algorithm performance with modified basic genetic operators. Intelligent operators improve overall algorithmpsilas behavior. In addition, algorithm performance is considerably improved by using cooperative genetic method.