DFS-tree based heuristic search

Authors:
Montserrat Abril;Miguel A. Salido;Federico Barber
Affiliations:
Dpt. of Information Systems and Computation, Technical University of Valencia, Valencia, Spain;Dpt. of Information Systems and Computation, Technical University of Valencia, Valencia, Spain;Dpt. of Information Systems and Computation, Technical University of Valencia, Valencia, Spain
Venue:
SARA'07 Proceedings of the 7th International conference on Abstraction, reformulation, and approximation
Year:
2007

Citing 5
Cited 1

A multilevel algorithm for partitioning graphs

Supercomputing '95 Proceedings of the 1995 ACM/IEEE conference on Supercomputing
A parallel algorithm for multilevel graph partitioning and sparse matrix ordering

Journal of Parallel and Distributed Computing
Algorithms for Distributed Constraint Satisfaction: A Review

Autonomous Agents and Multi-Agent Systems
Constraint Processing

Constraint Processing
Ants can solve constraint satisfaction problems

IEEE Transactions on Evolutionary Computation

Feasible distributed CSP models for scheduling problems

Engineering Applications of Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

In constraint satisfaction, local search is an incomplete method for finding a solution to a problem. Solving a general constraint satisfaction problem (CSP) is known to be NP-complete; so that heuristic techniques are usually used. The main contribution of this work is twofold: (i) a technique for de-composing a CSP into a DFS-tree CSP structure; (ii) an heuristic search technique for solving DFS-tree CSP structures. This heuristic search technique has been empirically evaluated with random CSPs. The evaluation results show that the behavior of our heuristic outperforms than the behavior of a centralized algorithm.