Constraint Programming is a powerful paradigm for solving Combinatorial Problems. In this solver approach, Enumeration Strategies are crucial for resolution performances. In a previous work, we proposed a framework to reactively change strategies showing bad performances, and to use metabacktracks to restore better states when bad decisions were made. In this paper, we design and evaluate strategies to improve resolution performances of a set of problems. Experimental results show the effectiveness of our approach. © 2009 IEEE.
|Number of pages||5|
|Publication status||Published - 1 Dec 2009|
|Event||8th Mexican International Conference on Artificial Intelligence - Proceedings of the Special Session, MICAI 2009 - |
Duration: 1 Dec 2009 → …
|Conference||8th Mexican International Conference on Artificial Intelligence - Proceedings of the Special Session, MICAI 2009|
|Period||1/12/09 → …|