Optimal speedup of Las Vegas algorithms
Information Processing Letters
Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
A fast taboo search algorithm for the job shop problem
Management Science
Dynamic problem structure analysis as a basis for constraint-directed scheduling heuristics
Artificial Intelligence
Algorithm Selection using Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A Bayesian Approach to Tackling Hard Computational Problems
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Robust and Parallel Solving of a Network Design Problem
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Learning the Empirical Hardness of Optimization Problems: The Case of Combinatorial Auctions
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Restart Policies with Dependence among Runs: A Dynamic Programming Approach
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Eighteenth national conference on Artificial intelligence
Empirical modeling and analysis of local search algorithms for the job-shop scheduling problem
Empirical modeling and analysis of local search algorithms for the job-shop scheduling problem
Using Cost Distributions to Guide Weight Decay in Local Search for SAT
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Efficient Multi-start Strategies for Local Search Algorithms
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Boosting distributed constraint satisfaction
Journal of Heuristics
Efficient multi-start strategies for local search algorithms
Journal of Artificial Intelligence Research
Using CBR to select solution strategies in constraint programming
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
An approach for dynamic split strategies in constraint solving
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Adaptive enumeration strategies and metabacktracks for constraint solving
ADVIS'06 Proceedings of the 4th international conference on Advances in Information Systems
Learning algorithm portfolios for parallel execution
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
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This paper addresses the question of allocating computational resources among a set of algorithms in order to achieve the best performance on a scheduling problem instance. Our primary motivation in addressing this problem is to reduce the expertise needed to apply constraint technology. Therefore, we investigate algorithm control techniques that make decision based only on observations of the improvement in solution quality achieved by each algorithm. We call our approach "low-knowledge" since it does not rely on complex prediction models. We show that such an approach results in a system that achieves significantly better performance than all of the pure algorithms without requiring additional human expertise. Furthermore the low knowledge approach achieves performance equivalent to a perfect high-knowledge classification approach.