Timetabling can be seen in many different aspects of life like work, education, traortation, and hobbies. In fact, modern and developed societies won't be imagined without using of timetabling. In addition, we have seen more speed and convenient on doing works and tasks in people lives since computer's invention. Timetabling and scheduling tasks which face with many constraints to optimization are such works. Course timetabling which is done at departments of a university on the beginning of any semester is an example of timetabling. Course timetabling which is done manually is time consuming and may not be optimized. One of the most important problems regarding to course timetabling is different set of constraints, which cause it perform in variety way at different departments. Many tasks have been done for performing timetabling which are using metaheuristic methods. In this thesis a method base on hybrid of improved memetic and simulated annealing algorithms is proposed. Improvement in memetic algorithm means heuristic initializes population and modification in crossover operator. The modified crossover operator is known as greedy crossover operator. Also, an operator which is called improvement is designed for improvement of created chromosomes and decrease of violation of constraints. In addition, utilization of simulated annealing will result to increase of the exploitive search ability of memetic algorithm. The experimental results which are based on standard data indicate that method is more efficient in comparison with some other new methods. This method also applied to electrical and computer faculty's data. Different combined methods of genetic and memetic algorithms with proposed operators are mentioned for investigation of our proposed algorithm in this thesis. The experimental results indicate our proposed method is more efficient in comparison with some other combined methods. Keywords: Memetic Algorithm, Local Search, Simulated Annealing Algorithm, University Course Timetabling Problem
