Disco - Novo - GoGo: integrating local search and complete search with restarts

Authors:
Meinolf Sellmann;Carlos Ansótegui
Affiliations:
Department of Computer Science, Brown University, Providence, RI;Artificial Intelligence Research Institute, IIIA-CSIC, Bellaterra, Spain
Venue:
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Year:
2006

Citing 9
Cited 3

Optimal speedup of Las Vegas algorithms

Information Processing Letters
The Adaptive Constraint Engine

CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
A Hybrid Approach for SAT

CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Constraint-Based Local Search

Constraint-Based Local Search
Complete local search for propositional satisfiability

AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Limited discrepancy search

IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Balance and filtering in structured satisfiable problems

IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Another complete local search method for SAT

LPAR'05 Proceedings of the 12th international conference on Logic for Programming, Artificial Intelligence, and Reasoning
A search-infer-and-relax framework for integrating solution methods

CPAIOR'05 Proceedings of the Second international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems

Solution counting algorithms for constraint-centered search heuristics

Constraints
Using expectation maximization to find likely assignments for solving CSP's

AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Solution-guided multi-point constructive search for job shop scheduling

Journal of Artificial Intelligence Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

A hybrid algorithm is devised to boost the performance of complete search on under-constrained problems. We suggest to use random variable selection in combination with restarts, augmented by a coarse-grained local search algorithm that learns favorable value heuristics over the course of several restarts. Numerical results show that this method can speed-up complete search by orders of magnitude.