Proceedings of the third international conference on Genetic algorithms
Graph Coloring with Adaptive Evolutionary Algorithms
Journal of Heuristics
Exact phase transitions in random constraint satisfaction problems
Journal of Artificial Intelligence Research
Domain-independent extensions to GSAT: solving large structured satisfiability problems
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
A conflict tabu search evolutionary algorithm for solving constraint satisfaction problems
EvoCOP'08 Proceedings of the 8th European conference on Evolutionary computation in combinatorial optimization
Weighting for godot: learning heuristics for GSAT
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
A tabu search evolutionary algorithm for solving constraint satisfaction problems
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
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Evolutionary algorithms have employed the SAW (Stepwise Adaptation of Weights) method in order to solve CSPs (Constraint Satisfaction Problems). This method originated in hill-climbing algorithms used to solve instances of 3-SAT by adapting a weight for each clause. Originally, adaptation of weights for solving CSPs was done by assigning a weight for each variable or each constraint. Here we investigate a SAW method which assigns a weight for each conflict. Two simple stochastic CSP solvers are presented. For both we show that constraint based SAW and conflict based SAW perform equally on easy CSP samples, but the conflict based SAW outperforms the constraint based SAW when applied to hard CSPs. Moreover, the best of the two suggested algorithms in its conflict based SAW version performs better than the best known evolutionary algorithm for CSPs that uses weight adaptation, and even better than the best known evolutionary algorithm for CSPs in general.