Dual viewpoint heuristics for binary constraint satisfaction problems
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
The Adaptive Constraint Engine
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
CSCLP'04 Proceedings of the 2004 joint ERCIM/CoLOGNET international conference on Recent Advances in Constraints
Experimental studies of variable selection strategies based on constraint weights
Journal of Algorithms
Improving the performance of vector hyper-heuristics through local search
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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“Heuristic synergy” refers to improvements in search performance when the decisions made by two or more heuristics are combined. This paper considers combinations based on products and quotients, and a less familiar form of combination based on weighted sums of ratings from a set of base heuristics, some of which result in definite improvements in performance. Then, using recent results from a factor analytic study of heuristic performance, which had demonstrated two main effects of heuristics involving either buildup of contention or look-ahead-induced failure, it is shown that heuristic combinations are effective when they are able to balance these two actions. In addition to elucidating the basis for heuristic synergy (or lack thereof), this work suggests that the task of understanding heuristic search depends on the analysis of these two basic actions.