Systematic and nonsystematic search strategies
Proceedings of the first international conference on Artificial intelligence planning systems
Amplification of Search Performance through Randomization of Heuristics
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Neighborhood selection by probabilistic filtering for load balancing in production scheduling
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
Domain-independent extensions to GSAT: solving large structured satisfiability problems
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Heuristic-biased stochastic sampling
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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The probabilistic filtering method filters out an unpromising candidate solution by conducting a simple preliminary evaluation before a complete evaluation in order to improve the efficiency of a local search. In this paper, we improve probabilistic filtering so that it can be applied in general to large-scaled optimization problems. As compared to the previous probabilistic filtering method, our enhanced version includes a scaling and truncation function to increase the discriminating power of probabilistic filtering and repair some defects of the previous bias function in adjusting the level of greediness. Experiments have shown that our method is more effective in improving the performance of a local search than the previous method. It has also been shown that the probabilistic filtering can be effective even when the preliminary evaluation heuristic is somewhat inaccurate, and the lesser the cost of preliminary evaluation, the greater is its effectiveness.