Local Search for Heuristic Guidance in Tree Search

Recent work has shown the promise in using local-search “probes” as a basis for directing a backtracking-based refinement search. In this approach, the decision about the next refinement step is based on an interposed phase of sampling possible (but not necessarily feasible) variable assignments by local search. This information is then used to decide on which refinement to take, i.e., as a kind of variable- and value-ordering strategy. In this paper, we investigate the efficiency of this hybrid search approach in the combinatorial domain of job-shop scheduling. First, we evaluate methods for improving probe-based guidance, by basing refinement decisions not only on the final assignment of the probe-construction phase but also on information gathered during the probeconstruction process. We show that such techniques can result in a significant performance boost. Second, we consider the relative strengths of probe-based search control and search control that is biased by more...

• Type: Conference
• Year: 2004
• Where: ECAI
• Authors: Alexander Nareyek, Stephen F. Smith, Christian M. Ohler
• Keywords: Artificial Intelligence, Backtracking-based Refinement Search, ECAI 2004, Local Search, Search Control