Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
On the run-time behaviour of stochastic local search algorithms for SAT
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
The Evolution of Customer Middleware Requirements
PDIS '94 Proceedings of the Third International Conference on Parallel and Distributed Information Systems
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
Solving Distributed Constraint Optimization Problems Using Cooperative Mediation
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Artificial Intelligence - Special issue: Distributed constraint satisfaction
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
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Some of the most successful algorithms for satisfiability, such as Walksat, are based on random walks. Similarly, local search algorithms for solving constraint optimization problems benefit significantly from randomization. However, well-known algorithms such as stochastic search or simulated annealing perform a less directed random walk than used in satisfiability. By making a closer analogy to the technique used in Walksat, we obtain a different kind of randomization called random subset optimization. Experiments on both structured and random problems show strong performance compared with other local search algorithms.