A survey of practical applications of examination timetabling algorithms
Operations Research
Computers and Operations Research
New methods to color the vertices of a graph
Communications of the ACM
Statistical Analysis: A Computer Oriented Approach
Statistical Analysis: A Computer Oriented Approach
Improving Evolutionary Timetabling with Delta Evaluation and Directed Mutation
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
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
A Constraint-Based Approach for Examination Timetabling Using Local Repair Techniques
PATAT '97 Selected papers from the Second International Conference on Practice and Theory of Automated Timetabling II
Examination Timetables and Tabu Search with Longer-Term Memory
PATAT '00 Selected papers from the Third International Conference on Practice and Theory of Automated Timetabling III
A Grouping Genetic Algorithm for Graph Colouring and Exam Timetabling
PATAT '00 Selected papers from the Third International Conference on Practice and Theory of Automated Timetabling III
Tabu Search Techniques for Examination Timetabling
PATAT '00 Selected papers from the Third International Conference on Practice and Theory of Automated Timetabling III
Fast Practical Evolutionary Timetabling
Selected Papers from AISB Workshop on Evolutionary Computing
New Algorithms for Examination Timetabling
WAE '00 Proceedings of the 4th International Workshop on Algorithm Engineering
A Tabu-Search Hyperheuristic for Timetabling and Rostering
Journal of Heuristics
Case-based heuristic selection for timetabling problems
Journal of Scheduling
Initialization strategies and diversity in evolutionary timetabling
Evolutionary Computation
Generating University Course Timetable Using Genetic Algorithms and Local Search
ICCIT '08 Proceedings of the 2008 Third International Conference on Convergence and Hybrid Information Technology - Volume 01
A multi-objective evolutionary algorithm for examination timetabling
Journal of Scheduling
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
Information Sciences: an International Journal
A perspective on bridging the gap between theory and practice in university timetabling
PATAT'06 Proceedings of the 6th international conference on Practice and theory of automated timetabling VI
A novel fuzzy approach to evaluate the quality of examination timetabling
PATAT'06 Proceedings of the 6th international conference on Practice and theory of automated timetabling VI
Linear linkage encoding in grouping problems: applications on graph coloring and timetabling
PATAT'06 Proceedings of the 6th international conference on Practice and theory of automated timetabling VI
Ant algorithms for the exam timetabling problem
PATAT'06 Proceedings of the 6th international conference on Practice and theory of automated timetabling VI
An experimental study on hyper-heuristics and exam timetabling
PATAT'06 Proceedings of the 6th international conference on Practice and theory of automated timetabling VI
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Fish swarm intelligent algorithm for the course timetabling problem
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Neighborhood analysis: a case study on curriculum-based course timetabling
Journal of Heuristics
A hybrid metaheuristic approach to the university course timetabling problem
Journal of Heuristics
Dual sequence simulated annealing with round-robin approach for university course timetabling
EvoCOP'10 Proceedings of the 10th European conference on Evolutionary Computation in Combinatorial Optimization
A hybrid fish swarm optimisation algorithm for solving examination timetabling problems
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
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
Fuzzy multiple heuristic orderings for examination timetabling
PATAT'04 Proceedings of the 5th international conference on Practice and Theory of Automated Timetabling
A multistage evolutionary algorithm for the timetable problem
IEEE Transactions on Evolutionary Computation
Information Sciences: an International Journal
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Finding a good university timetabling system is not a simple task for a higher educational organisation. As a result, many approaches to generating sufficiently good solutions have been introduced. This is mainly due to the high complexity within the search landscape; moreover, each educational organisation has its own rules and specifications. In this paper, a Tabu-based memetic algorithm that hybridises a genetic algorithm with a Tabu Search algorithm is proposed as an improved algorithm for university timetabling problems. This algorithm is employed on a set of neighbourhood structures during the search process with the aim of gaining significant improvements in solution quality. The sequence of neighbourhood structures has been considered to understand its effect on the search space. Random, best and general sequences of neighbourhood structures have been evaluated in this work. The concept of a Tabu list is embedded to control the selection of neighbourhood structures that are not dependent on the problem domains during the optimisation process after the crossover and mutation operators are applied to the selected solutions from the population pool. The algorithm will penalise neighbourhood structures that are unable to generate better solutions. The proposed algorithm has been applied and evaluated against the latest methodologies in the literature with respect to standard benchmark problems. We demonstrate that the proposed algorithm produces some of the best known results when tested on ITC2007 competition datasets.