Hopfield neural networks for timetabling: formulations, methods, and comparative results
Computers and Industrial Engineering - Special issue: Focussed issue on applied meta-heuristics
Applying evolutionary computation to the school timetabling problem: The Greek case
Computers and Operations Research
Proceedings of the 2008 annual research conference of the South African Institute of Computer Scientists and Information Technologists on IT research in developing countries: riding the wave of technology
An application of genetic algorithms to the school timetabling problem
Proceedings of the 2008 annual research conference of the South African Institute of Computer Scientists and Information Technologists on IT research in developing countries: riding the wave of technology
Computers and Operations Research
School timetabling for quality student and teacher schedules
Journal of Scheduling
The KTS high school timetabling system
PATAT'06 Proceedings of the 6th international conference on Practice and theory of automated timetabling VI
A comparative study of hyper-heuristics for solving the school timetabling problem
Proceedings of the South African Institute for Computer Scientists and Information Technologists Conference
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Research in the domain of school timetabling has not developed as rapidly as other areas of educational timetabling such as university course and examination timetabling. A lot of research has been conducted into using hyper-heuristics to solve the university course and examination timetabling problem, however this area has not been investigated for school timetabling. The aim of hyper-heuristics is to generalize well over a set of problems for a particular domain, rather than producing the best result for one or more problems. Hyper-heuristics search a heuristic space instead of a space of potential solutions to the problem. This study is a first attempt at applying hyper-heuristics to the school timetabling problem. The study compares the performance of different low-level construction heuristics for this domain. An evolutionary algorithm (EA) is implemented to search the space of heuristic combinations. The study has also revealed that the use of "nondestructive" mutation and crossover operators, which incorporate the use of hill-climbing, improves the performance of the EA-based hyper-heuristic. The performance of the EA hyper-heuristic in solving the school timetabling problem is also compared to other methods applied to the same problem.