Abstract
Theoretical models for the evaluation of quickly improving search strategies, like limited discrepancy search, are based on specific assumptions regarding the probability that a value selection heuristic makes a correct prediction. We provide an extensive empirical evaluation of value selection heuristics for knapsack problems. We investigate how the accuracy of search heuristics varies as a function of depth in the search-tree, and how the accuracies of heuristic predictions are affected by the relative strength of inference methods like pruning and constraint propagation.
This work was supported by the National Science Foundation through the Career: Cornflower Project (award number 0644113).
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Leventhal, D.H., Sellmann, M. (2008). The Accuracy of Search Heuristics: An Empirical Study on Knapsack Problems. In: Perron, L., Trick, M.A. (eds) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems. CPAIOR 2008. Lecture Notes in Computer Science, vol 5015. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68155-7_13
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DOI: https://doi.org/10.1007/978-3-540-68155-7_13
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