A Comparison of a Heuristic and a Hopfield Neural Network Approach for Solving Examination Timetabling Problems
Abstract: The Examination Timetabling Problem (ETP) is the problem of scheduling a number of exams during a set time period so that no students are required to sit two exams simultaneously. Despite the complexity of the problem, universities all over the world solve ETPs several times each year. Two known methods for solving ETPs is using either heuristics or Hopfield Neural Networks (HNN). This thesis compares the performance of a heuristic algorithm implemented with Local Search, Simulated Annealing and Tabu Search to the performance of a HNN algorithm. Both algorithms were executed on ten different ETPs reduced to Graph Colouring Problems (GCP). The results show that the heuristic algorithm always generated more satisfactory solutions to the ETPs than the HNN. The HNN was, however, implemented as software in this thesis. It is intended to be implemented as hardware and if this method were to have been used instead the HNN algorithm might have produced other results. At this stage the heuristic algorithm is more suitable than the HNN algorithm for solving ETPs.
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