Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Metaheuristics for High School Timetabling
Computational Optimization and Applications
Artificial Intelligence: Structures and Strategies for Complex Problem Solving
Artificial Intelligence: Structures and Strategies for Complex Problem Solving
Hopfield neural networks for timetabling: formulations, methods, and comparative results
Computers and Industrial Engineering - Special issue: Focussed issue on applied meta-heuristics
A Constructive Evolutionary Approach to School Timetabling
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
A Hybrid Genetic Algorithm for School Timetabling
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Tabu search techniques for large high-school timetabling problems
Tabu search techniques for large high-school timetabling problems
Applying evolutionary computation to the school timetabling problem: The Greek case
Computers and Operations Research
The KTS high school timetabling system
PATAT'06 Proceedings of the 6th international conference on Practice and theory of automated timetabling VI
A study into the use of hyper-heuristics to solve the school timetabling problem
SAICSIT '10 Proceedings of the 2010 Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists
An informed genetic algorithm for the high school timetabling problem
SAICSIT '10 Proceedings of the 2010 Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists
The Interleaved Constructive Memetic Algorithm and its application to timetabling
Computers and Operations Research
Academic course scheduling by simulated annealing
Journal of Computing Sciences in Colleges
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There has been a large amount of research into the automatic generation of school timetables. Methodologies such as constraint programming, simulated annealing, Tabu search and genetic algorithms have been applied to the school timetabling problem. However, a majority of these studies focus on solving the problem for a particular school and there is very little research into the comparison of the performance of different techniques in solving the school timetabling problem. The study presented in this paper evaluates genetic algorithms (GAs) for the purpose of inducing school timetables. For each problem, the GA implemented iteratively refines an initial population of school timetables using mutation to find a good quality feasible timetable. The performance of the GA on a set of five benchmark problems has been compared to the performance of neural networks, simulated annealing, Tabu search, and greedy search on the same set of problems. The results obtained by the GA were found to be comparable to and an improvement on those produced by the other methods.