The problem of assigning students to course sections in a large engineering school
Computers and Operations Research
A large scale timetabling problem
Computers and Operations Research
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
A Genetic Algorithm Solving a Weekly Course-Timetabling Problem
Selected papers from the First International Conference on Practice and Theory of Automated Timetabling
A Comprehensive Course Timetabling and Student Scheduling System at the University of Waterloo
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
A MAX-MIN Ant System for the University Course Timetabling Problem
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
A Heuristic Incremental Modeling Approach to Course Timetabling
AI '98 Proceedings of the 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Fast Practical Evolutionary Timetabling
Selected Papers from AISB Workshop on Evolutionary Computing
Specialised Recombinative Operators for Timetabling Problems
Selected Papers from AISB Workshop on Evolutionary Computing
Tabu search techniques for large high-school timetabling problems
Tabu search techniques for large high-school timetabling problems
Initialization strategies and diversity in evolutionary timetabling
Evolutionary Computation
Minimal perturbation problem in course timetabling
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
Local search techniques for large high school timetabling problems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Case-based heuristic selection for timetabling problems
Journal of Scheduling
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
Modeling and solution of a complex university course timetabling problem
PATAT'06 Proceedings of the 6th international conference on Practice and theory of automated timetabling VI
The teaching space allocation problem with splitting
PATAT'06 Proceedings of the 6th international conference on Practice and theory of automated timetabling VI
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In this paper a new student sectioning algorithm is proposed. In this method a fuzzy clustering, a fuzzy evaluator and a novel feature selection method is used. Each student has a feature vector, contains his taken courses as its feature elements. The best features are selected for sectioning based on removing those courses that the most or the fewest numbers of students have taken. The Fuzzy c-Means classifier classifies students. After that, a fuzzy function evaluates the produced clusters based on two criteria: balancing sections and students' schedules similarity within each section. These are used as linguistic variables in a fuzzy inference engine. The selected features determine the best students' sections. Simulation results show that improvement in sectioning performance is about 18% in comparison with considering all of the features, which not only reduces the feature vector elements but also increases the computing performance.