On the Efficiency of Parallel Backtracking
IEEE Transactions on Parallel and Distributed Systems
Optimization-based Heuristics for Maximal Constraint Satisfaction
CP '95 Proceedings of the First International Conference on Principles and Practice of Constraint Programming
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Improved limited discrepancy search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Scheduling Tasks on Unrelated Machines: Large Neighborhood Improvement Procedures
Journal of Heuristics
Annals of Mathematics and Artificial Intelligence
A decomposition-based implementation of search strategies
ACM Transactions on Computational Logic (TOCL)
A unified framework for partial and hybrid search methods in constraint programming
Computers and Operations Research
Propositional Satisfiability and Constraint Programming: A comparative survey
ACM Computing Surveys (CSUR)
Incomplete tree search using adaptive probing
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
An improvement for the dynamic distributed double guided genetic algorithm for Max-CSPs
PDCS '07 Proceedings of the 19th IASTED International Conference on Parallel and Distributed Computing and Systems
Search strategies for an anytime usage of the branch and prune algorithm
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Confidence-based work stealing in parallel constraint programming
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Programming constraint services: high-level programming of standard and new constraint services
Programming constraint services: high-level programming of standard and new constraint services
Anytime AND/OR depth-first search for combinatorial optimization
AI Communications - The Symposium on Combinatorial Search
Load Balancing for the Dynamic Distributed Double Guided Genetic Algorithm for MAX-CSPs
International Journal of Artificial Life Research
Hi-index | 0.00 |
In tree search, depth-first search (DFS) often uses ordering successor heuristics. If the heuristic makes a mistake ordering a bad successor (without goals in its subtree) before good ones (with goals in their subtrees), DFS has to unsuccessfully traverse the whole bad subtree before finding a goal. To prevent this useless work, we present a new strategy called interleaved depthfirst search (IDFS), which searches depth-first several subtrees -- called active -- in parallel. IDFS assumes a single processor on which it interleaves DFS on active subtrees. When IDFS finds a mistake, it traverses partially the bad subtree. IDFS does not reexpand nodes and uses a memory amount linear in search depth (with a bounded number of active subtrees). IDFS outperforms DFS if the heuristic improves from the first to the second tree level. Experimental results on hard solvable problems confirm the practical validity of IDFS.