Adaptive enumeration strategies and metabacktracks for Constraint solving. Castro, C. I., Monfroy, É., & Crawford, B. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 4243 LNCS, pages 354-363, 2006.
abstract   bibtex   
In Constraint Programming, enumeration strategies are crucial for resolution performances. The effect of strategies is generally unpredictable. In a previous work, we proposed to dynamically change strategies showing bad performances, and to use metabacktrack 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. © Springer-Verlag Berlin Heidelberg 2006.
@inproceedings{33751382311,
    abstract = "In Constraint Programming, enumeration strategies are crucial for resolution performances. The effect of strategies is generally unpredictable. In a previous work, we proposed to dynamically change strategies showing bad performances, and to use metabacktrack 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. © Springer-Verlag Berlin Heidelberg 2006.",
    year = "2006",
    title = "Adaptive enumeration strategies and metabacktracks for Constraint solving",
    volume = "4243 LNCS",
    pages = "354-363",
    booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
    author = "Castro, Carlos Ivan and Monfroy, Éric and Crawford, Broderick"
}
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