Name
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-68155-7_13 | CPAIOR |
Keywords | Field | DocType |
value selection heuristic,search strategy,knapsack problem,extensive empirical evaluation,constraint propagation,inference method,correct prediction,heuristic prediction,limited discrepancy search,empirical study,value selection heuristics,search heuristics,optimization problem | Mathematical optimization,Incremental heuristic search,Local consistency,Heuristic,Brute-force search,Computer science,Beam search,Continuous knapsack problem,Heuristics,Artificial intelligence,Knapsack problem,Machine learning | Conference |
Volume | ISSN | ISBN |
5015 | 0302-9743 | 3-540-68154-X |
Citations | PageRank | References |
3 | 0.40 | 13 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel H. Leventhal | 1 | 14 | 1.41 |
| Meinolf Sellmann | 2 | 728 | 48.77 |