Article ID: | iaor20032965 |
Country: | United States |
Volume: | 139 |
Issue: | 1 |
Start Page Number: | 21 |
End Page Number: | 45 |
Publication Date: | Jul 2002 |
Journal: | Artificial Intelligence |
Authors: | Jussien N., Lhomme O. |
Keywords: | artificial intelligence |
Search algorithms for solving CSP (Constraint Satisfaction Problems) usually fall into one of two main families: local search algorithms and systematic algorithms. Both families have their advantages. Designing hybrid approaches seems promising since those advantages may be combined into a single approach. In this paper, we present a new hybrid technique. It performs a local search over partial assignments instead of complete assignments, and uses filtering techniques and conflict-based techniques to efficiently guide the search. This new technique benefits from both classical approaches: a priori pruning of the search space from filtering-based search and possible repair of early mistakes from local search. We focus on a specific version of this technique: tabu decision–repair. Experiments done on open-shop sheduling problems show that our approach competes well with the best highly specialized algorithms.