Abstract. The course timetabling problem deals with the assignment of a set of courses to specific timeslots and rooms within a working week subject to a variety of hard and soft constraints. Solutions are called feasible if all the hard constraints are satisfied. The goal is to satisfy as many of the soft constraints as possible whilst constructing a feasible schedule. In this paper, we present a combination of two metaheuristics i.e. great deluge and tabu search approaches. The algorithm is tested over eleven benchmark datasets (representing one large, five medium and five small problems). The results demonstrate that our approach is able to produce solutions that have lower penalty on all the small and medium problems when compared against other techniques from the literature.

The goal of np-hard Combinatorial Optimization is finding the best possible solution from the set of feasible solutions. In this paper, we establish an approach using genetic algorithm with various selection and crossover operators with repair function for an institute course timetabling problem. It employs a constructive heuristic approach to find the feasible timetable, fitness value calculation, selection operators, crossover operators and repair function. The performance of proposed and existing selection and crossover operators are compared and shown by keeping diversity in the fitness value of population.

Abstract- Course Timetabling is a complex problem, happening at the beginning of every semester at universities. In this problem, one of the most important issues is variety of constraints, which results in different ways of timetabling in different universities. Comparing to exact methods which take lots of time to solve UCTP, metaheuristic methods produce a feasible solution within reasonable computation time. In this paper, a hybrid method is presented, which is based on combination of improved Memetic and Simulated Annealing Algorithms. Using Simulated Annealing Algorithm as the local search routine increases exploiting ability of Memetic Algorithm. Also, modifying Crossover operator of Memetic Algorithm and creating initial population by a heuristic-based method improves this algorithm. In order to improve produced chromosomes and decreasing the number of violation of the constraints, a new operator is designed and added to Memetic Algorithm called improvement operator. With comparing the results of this method and some modern methods using standard data, efficiency of this method is clear.

The university course timetabling problem (UCTP) is a combinatorial optimization problem, in which a set of events has to be scheduled into time slots and located into suitable rooms. The design of course timetables for academic institutions is a very difficult task because it is an NP-hard problem. This paper investigates genetic algorithms (GAs) with a guided search strategy and local search (LS) techniques for the UCTP. The guided search strategy is used to create offspring into the population based on a data structure that stores information extracted from good individuals of previous generations. The LS techniques use their exploitive search ability to improve the search efficiency of the proposed GAs and the quality of individuals. The proposed GAs are tested on two sets of benchmark problems in comparison with a set of state-ofthe-art methods from the literature. The experimental results show that the proposed GAs are able to produce promising results for the UCTP.

Abstract—The university course timetabling problem (UCTP) is a combinatorial optimization problem, in which a set of events has to be scheduled into time slots and located into suitable rooms. The design of course timetables for academic institutions is a very difficult task because it is an NP-hard problem. This paper investigates genetic algorithms (GAs) with a guided search strategy and local search (LS) techniques for the UCTP. The guided search strategy is used to create offspring into the population based on a data structure that stores information extracted from good individuals of previous generations. The LS techniques use their exploitive search ability to improve the search efficiency of the proposed GAs and the quality of individuals. The proposed GAs are tested on two sets of benchmark problems in comparison with a set of state-ofthe-art methods from the literature. The experimental results show that the proposed GAs are able to produce promising results for the UCTP. Index Terms—Genetic algorithm (GA), guided search, local search (LS), university course timetabling problem (UCTP). I.

The design of course timetables for academic institutions is a very hectic job due to the exponential number of possible feasible timetables with respect to the problem size. This process involves lots of constraints that must be respected and a huge search space to be explored, even if the size of the problem input is not significantly large. On the other hand, the problem itself does not have a widely approved definition, since different institutions face different variations of the problem. This paper presents a memetic algorithm that integrates two local search methods into the genetic algorithm for solving the university course timetabling problem (UCTP). These two local search methods use their exploitive search ability to improve the explorative search ability of genetic algorithms. The experimental results indicate that the proposed memetic algorithm is efficient for solving the UCTP. 1