Incorporating constraint propagation in genetic algorithm for university timetable planning

Abstract

Timetable planning can be modelled as a constraint-satisfaction problem, and may be solved by various approaches, including genetic algorithms. An optimal solution for a timetable planning problem is difficult to find using genetic algorithms, due to the ambiguity in deciding the fitness function. Various approaches aimed at finding optimal solutions to constraint-satisfaction problems by genetic algorithms have been proposed, but most of these approaches are problem-dependent and hence are difficult to apply to real-world problems. In this paper, a hybrid algorithm consisting of a genetic algorithm and constraint-based reasoning is proposed to find a feasible and near-optimal solution. The proposed algorithm was tested by using real data for university timetable planning, and this approach can be applied to most constraint-satisfaction problems.