Algorithms (2nd ed.)
Computers and Operations Research
Local search characteristics of incomplete SAT procedures
Artificial Intelligence
Tabu Search
A Survey of Automated Timetabling
Artificial Intelligence Review
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
An effective hybrid algorithm for university course timetabling
Journal of Scheduling
Progressive Tree Neighborhood Applied to the Maximum Parsimony Problem
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A Critical Element-Guided Perturbation Strategy for Iterated Local Search
EvoCOP '09 Proceedings of the 9th European Conference on Evolutionary Computation in Combinatorial Optimization
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 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
Information Sciences: an International Journal
Proceedings of the 14th annual conference on Genetic and evolutionary computation
A Complementary Cyber Swarm Algorithm
International Journal of Swarm Intelligence Research
A memetic algorithm for the Minimum Sum Coloring Problem
Computers and Operations Research
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In this paper, we present an in-depth analysis of neighborhood relations for local search algorithms. Using a curriculum-based course timetabling problem as a case study, we investigate the search capability of four neighborhoods based on three evaluation criteria: percentage of improving neighbors, improvement strength and search steps. This analysis shows clear correlations of the search performance of a neighborhood with these criteria and provides useful insights on the very nature of the neighborhood. This study helps understand why a neighborhood performs better than another one and why and how some neighborhoods can be favorably combined to increase their search power. This study reduces the existing gap between reporting experimental assessments of local search-based algorithms and understanding their behaviors.