Improved CLP scheduling with task intervals
Proceedings of the eleventh international conference on Logic programming
Boosting combinatorial search through randomization
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A Meta-heuristic for Subset Problems
PADL '01 Proceedings of the Third International Symposium on Practical Aspects of Declarative Languages
Principles of Constraint Programming
Principles of Constraint Programming
Low-knowledge algorithm control
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
A component language for hybrid solver cooperations
ADVIS'04 Proceedings of the Third international conference on Advances in Information Systems
Adaptive hybridization strategies
Proceedings of the 2011 ACM Symposium on Applied Computing
Adaptive enumeration strategies and metabacktracks for constraint solving
ADVIS'06 Proceedings of the 4th international conference on Advances in Information Systems
Parameter tuning of a choice-function based hyperheuristic using Particle Swarm Optimization
Expert Systems with Applications: An International Journal
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In constraint programming, a priori choices statically determine strategies that are crucial for resolution performances. However, the effect of strategies is generally unpredictable. We propose to dynamically change strategies showing bad performances. When this is not enough to improve resolution, we introduce some meta-backtracks. Our goal is to get good performances without the know-how of experts. Some first experimental results show the effectiveness of our approach.