YIELDS: a yet improved limited discrepancy search for CSPs. In this paper, we introduce a Yet ImprovEd Limited Discrepancy Search (YIELDS), a complete algorithm for solving Constraint Satisfaction Problems. As indicated in its name, YIELDS is an improved version of Limited Discrepancy Search (LDS). It integrates constraint propagation and variable order learning. The learning scheme, which is the main contribution of this paper, takes benefit from failures encountered during search in order to enhance the efficiency of variable ordering heuristic. As a result, we obtain a search which needs less discrepancies than LDS to find a solution or to state a problem is intractable. This method is then less redundant than LDS. The efficiency of YIELDS is experimentally validated, comparing it with several solving algorithms: Depth-bounded Discrepancy Search, Forward Checking, and Maintaining Arc Consistency. Experiments carried out on randomly generated binary CSPs and real problems clearly indicate that YIELDS often outperforms the algorithms with which it is compared, especially for tractable problems.
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References in zbMATH (referenced in 4 articles , 1 standard article )
Showing results 1 to 4 of 4.
- Ouali, Abdelkader; Allouche, David; de Givry, Simon; Loudni, Samir; Lebbah, Yahia; Loukil, Lakhdar; Boizumault, Patrice: Variable neighborhood search for graphical model energy minimization (2020)
- Huguet, Marie-José; Lopez, Pierre; Karoui, Wafa: Weight-based heuristics for constraint satisfaction and combinatorial optimization problems (2012)
- Gacias, Bernat; Artigues, Christian; Lopez, Pierre: Parallel machine scheduling with precedence constraints and setup times (2010)
- Karoui, Wafa; Huguet, Marie-José; Lopez, Pierre; Naanaa, Wady: YIELDS: a yet improved limited discrepancy search for CSPs (2007)