Abstract
We present a two-phase genetic algorithm (TGA) to solve timetabling problems for universities.1 Here, we use two kinds of populations. The first population is related to class scheduling, and the second one is related to room allocation. These populations are evolved independently, and a cost value of each individual is calculated. Then several individuals with the lowest costs are selected from each population, and these individuals are combined in order to calculate the fitness values. To evaluate the performance of TGA, we apply TGA to several timetabling problems and compare results obtained by TGA with those obtained by the simple GA (SGA). For the timetabling problem based on the curriculum of the Faculty of Information Sciences at Hiroshima City University, TGA finds a solution that satisfies all constraints, but SGA cannot find a feasible solution. From the results for problems generated by an automatic timetabling problem generator, we show that TGA obtains a better solution than the simple GA when the room utilization ratio is high.
This research is partly supported by Research Grant from Hiroshima City University.
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Ueda, H., Ouchi, D., Takahashi, K., Miyahara, T. (2001). A Co-Evolving Timeslot/Room Assignment Genetic Algorithm Technique for University Timetabling. In: Burke, E., Erben, W. (eds) Practice and Theory of Automated Timetabling III. PATAT 2000. Lecture Notes in Computer Science, vol 2079. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44629-X_4
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DOI: https://doi.org/10.1007/3-540-44629-X_4
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