Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
A Memetic Algorithm for University Exam Timetabling
Selected papers from the First International Conference on Practice and Theory of Automated Timetabling
Some Observations about GA-Based Exam Timetabling
PATAT '97 Selected papers from the Second International Conference on Practice and Theory of Automated Timetabling II
Ant algorithms for the exam timetabling problem
PATAT'06 Proceedings of the 6th international conference on Practice and theory of automated timetabling VI
A hybrid multi-objective evolutionary algorithm for the uncapacitated exam proximity problem
PATAT'04 Proceedings of the 5th international conference on Practice and Theory of Automated Timetabling
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
An evolutionary-based approach for solving a capacitated hub location problem
Applied Soft Computing
A memetic algorithm for course timetabling
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A hybrid evolutionary algorithm for tuning a cloth-simulation model
Applied Soft Computing
On the application of bio-inspired algorithms in timetabling problem
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Course timetabling using evolutionary operators
Applied Soft Computing
The classroom assignment problem: Complexity, size reduction and heuristics
Applied Soft Computing
Evolutionary algorithms using cluster patterns for timetabling
Intelligent Decision Technologies
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This paper presents the results of a study conducted to investigate the use of genetic algorithms (GAs) as a means of inducing solutions to the examination timetabling problem (ETP). This study differs from previous efforts applying genetic algorithms to this domain in that firstly it takes a two-phased approach to the problem which focuses on producing timetables that meet the hard constraints during the first phase, while improvements are made to these timetables in the second phase so as to reduce the soft constraint costs. Secondly, domain specific knowledge in the form of heuristics is used to guide the evolutionary process. The system was tested on a set of 13 real-world problems, namely, the Carter benchmarks. The performance of the system on the benchmarks is comparable to that of other evolutionary techniques and in some cases the system was found to outperform these techniques. Furthermore, the quality of the examination timetables evolved is within range of the best results produced in the field.