Communications of the ACM
Learning to solve problems by searching for macro-operators
Learning to solve problems by searching for macro-operators
A survey of practical applications of examination timetabling algorithms
Operations Research
A Critical Look at Experimental Evaluations of EBL
Machine Learning
Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
C4.5: programs for machine learning
C4.5: programs for machine learning
Information Filtering: Selection Mechanisms in Learning Systems
Machine Learning
Statistical Methods for Analyzing Speedup Learning Experiments
Machine Learning
Learning Search Control Knowledge: An Explanation-Based Approach
Learning Search Control Knowledge: An Explanation-Based Approach
Bayes networks for estimating the number of solutions of constraint networks
Annals of Mathematics and Artificial Intelligence
A Heuristic Approach to the Discovery of Macro-Operators
Machine Learning
Machine Learning
Employee Timetabling, Constraint Networks and Knowledge-Based Rules: A Mixed Approach
Selected papers from the First International Conference on Practice and Theory of Automated Timetabling
Experiments on Networks of Employee Timetabling Problems
PATAT '97 Selected papers from the Second International Conference on Practice and Theory of Automated Timetabling II
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
Modelling and Solving Employee Timetabling Problems
Annals of Mathematics and Artificial Intelligence
A survey of automated timetabling
A survey of automated timetabling
Learning evaluation functions to improve optimization by local search
The Journal of Machine Learning Research
Automatic generation of heuristics for scheduling
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Adaptive problem-solving for large-scale scheduling problems: a case study
Journal of Artificial Intelligence Research
A formal framework for speedup learning from problems and solutions
Journal of Artificial Intelligence Research
A selective macro-learning algorithm and its application to the N × N sliding-tile puzzle
Journal of Artificial Intelligence Research
A reinforcement learning approach to job-shop scheduling
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Tabu search techniques for large high-school timetabling problems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Learning as applied to stochastic optimization for standard-cell placement
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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Repair-based search algorithms start with an initial solution and attempt to improve it by iteratively applying repair operators. Such algorithms can often handle large-scale problems that may be difficult for systematic search algorithms. Nevertheless, the computational cost of solving such problems is still very high. We observed that many of the repair steps applied by such algorithms are redundant in the sense that they do not eventually contribute to finding a solution. Such redundant steps are particularly harmful in repair-based search, where each step carries high cost due to the very high branching factor typically associated with it. Accurately identifying and avoiding such redundant steps would result in faster local search without harming the algorithm's problem-solving ability. In this paper we propose a speedup learning methodology for attaining this goal. It consists of the following steps: defining the concept of a redundant step; acquiring this concept during off-line learning by analyzing solution paths for training problems, tagging all the steps along the paths according to the redundancy definition and using an induction algorithm to infer a classifier based on the tagged examples; and using the acquired classifier to filter out redundant steps while solving unseen problems. Our algorithm was empirically tested on instances of real-world employee timetabling problems (ETP). The problem solver to be improved is based on one of the best methods for solving some large ETP instances. Our results show a significant improvement in speed for test problems that are similar to the given example problems.