Abstract
Problem of courses timetabling is a time consuming and demanding issues in any education environment that they are involved in every semester. The main aim of timetabling problem is the allocation of a number of courses to a limited set of resources such as classrooms, time slots, professors and students so that some predefined hard and soft constraints are satisfied. Furthermore, the available resources are used to the best.   In fact course timetabling is one of optimization problems. It has been proved computational complexity of this problem is NP, so there is no optimal solution for that. Therefore, approximation and heuristic techniques are used to find near optimal solutions. Genetic algorithm for its multidirectional feature has been one of the most widely used approaches in recent years. Hence, in this paper an improved genetics algorithm for timetabling problem has been proposed. In proposed algorithm, the fitness of solutions to satisfy soft constraints due to ambiguous nature of those has been specified using fuzzy logic. Also, local search methods have been applied to avoid the genetic algorithm to be trapped in a local optimum. As well as, the multi-population property is intended to reduce the time to reach the optimum solution.  Evaluation results show that the proposed solutions are able to produce promising results for the university courses timetabling.
Highlights
The course timetabling problem is one of the complex and time consuming issues in universities that they are involved at the beginning of each semester
We investigated the performance of the following algorithms: Conventional genetic algorithm: This is the algorithm that addressed in figure 1 without applying local search algorithm
In this paper we have introduced an algorithm for university course timetabling problem
Summary
The course timetabling problem is one of the complex and time consuming issues in universities that they are involved at the beginning of each semester. The goal is to construct a timetable in which the highest number of conflicting events are scheduled in feasible time slots and respect soft constraints as possible [4,5,6,7,8]. These algorithms work efficiently for small-scale scheduling problems, but they are not effective in large scale [1, 4]. In proposed GA, after applying mutation and crossover operators to generate new solutions, a local search algorithm (iterative search in this study) is used.
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