Abstract:
The university course timetabling problem consists of allocating a number of courses to a limited set of resources such as rooms, timeslots, set of lecturers and group of students in such a way to satisfy predefined constraints. The constraints can be divided into two groups: hard constraints and soft constraints. A timetable has to satisfy all hard constraints in order to be feasible and it should satisfy as much as possible soft constraints in order to be in good quality.
The university timetabling problem is in the class of NP problems. This means that the amount of time and work required solving this type of problems increases exponentially with the problem size. This makes these problems more difficult and time consuming. Therefore optimization techniques are used to solve them and produce optimal or near optimal feasible solutions .Steady State Genetic algorithms are considered as an efficient approach for solving this type of problems.
In this thesis, a general description of genetic algorithms and theoretical background, a real life university course timetabling problem is examined with data from Sudan University of Science & Technology, College of computer Science and Information Technology. A solution based on Steady State genetic algorithm is proposed for the problem; implementations are described and applied to the sample problem.
Moreover, determination of the best parameter values, such as the population size, mutation and crossover rate, etc. is tried. In the proposed system we used C++ programming language.
