Genetic Algorithms for timetable Generation
Thesis
Timetabling presents an NP-hard combinatorial optimization problem which requires an efficient search algorithm. This research aims at designing a genetic algorithm for timetabling real-world school resources to fulfil a given set of constraints and preferences. It further aims at proposing a parallel algorithm that is envisaged to speed up convergence to an optimal solution, given its existence. The timetable problem is modeled as a constraint satisfaction problem (CSP) and a theoretical framework is proposed, which guides the approach used to formulate the algorithm. The constraints are expressed mathematically and a conventional algorithm is designed that evaluates solution fitness based on these constraints. Test results based on a subset of real-world, working data indicate that convergence on a feasible (and optimal/Pareto) solution is possible within the search space presented by the given resources and constraints. The algorithm also degrades gracefully to a workable timetable if an optimal one is not located. Further, a SIMD-based parallel algorithm is proposed that has the potential to speed up convergence on multi-processor or distributed platforms.