Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Reactive search, a history-sensitive heuristic for MAX-SAT
Journal of Experimental Algorithmics (JEA)
Artificial Intelligence
The complexity of theorem-proving procedures
STOC '71 Proceedings of the third annual ACM symposium on Theory of computing
Domain-independent extensions to GSAT: solving large structured satisfiability problems
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Approximation algorithms for combinatorial problems
Journal of Computer and System Sciences
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Evidence for invariants in local search
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Population-Based Extremal Optimization with Adaptive Lévy Mutation for Constrained Optimization
Computational Intelligence and Security
A backbone-based co-evolutionary heuristic for partial MAX-SAT
EA'05 Proceedings of the 7th international conference on Artificial Evolution
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Stochastic local search algorithms are proved to be one of the most effective approach for computing approximate solutions of hard combinatorial problems. Most of them are based on a typical randomness related to uniform distributions for generating initial solutions. Particularly, Extremal Optimization is a recent metaheuristic proposed for finding high quality solutions to hard optimization problems. In this paper, we introduce an algorithm based on another distribution, known as the Bose-Einstein distribution in quantum physics, which provides a new stochastic initialization scheme to an Extremal Optimization procedure. The resulting algorithm is proposed for the approximated solution to an instance of the weighted maximum satisfiability problem (MAXSAT). We examine its effectiveness by computational experiments on a large set of test instances and compare it with other existing meta-heuristic methods. Our results are remarkable and show that this approach is appropriate for this class of problems.