A Software Engineering Approach to University Timetabling
MSE '00 Proceedings of the 2000 International Conference on Microelectronic Systems Education
Using Hyper-heuristics for the Dynamic Variable Ordering in Binary Constraint Satisfaction Problems
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Pipelining Memetic Algorithms, Constraint Satisfaction, and Local Search for Course Timetabling
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
A conflict tabu search evolutionary algorithm for solving constraint satisfaction problems
EvoCOP'08 Proceedings of the 8th European conference on Evolutionary computation in combinatorial optimization
Graphs & Digraphs, Fifth Edition
Graphs & Digraphs, Fifth Edition
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The aim of hyper-heuristics is to solve a wide range of problem instances with a set of heuristics, each chosen according to the specific characteristics of the problem. In this paper, our intention is to explore two different heuristics to segment the Course Timetabling Problem (CTT) into subproblems with the objective of solving them efficiently. Each subproblem is resolved as a Constraint Satisfaction Problem (CSP). Once the CTT is partitioned and each part solved separately, we also propose two different strategies to integrate the solutions and get a complete assignment. Both integration strategies use a Min-Conflicts algorithm to reduce the inconsistencies that might arise during this integration. Each problem instance was solved with and without segmentation. The results show that simple problems do not benefit with the use of segmentation heuristics, whilst harder problems have a better behavior when we use these heuristics.