Unweighted Stochastic Local Search can be Effective for Random CSP Benchmarks
Artificial Intelligence
2014-12-01 v1
Abstract
We present ULSA, a novel stochastic local search algorithm for random binary constraint satisfaction problems (CSP). ULSA is many times faster than the prior state of the art on a widely-studied suite of random CSP benchmarks. Unlike the best previous methods for these benchmarks, ULSA is a simple unweighted method that does not require dynamic adaptation of weights or penalties. ULSA obtains new record best solutions satisfying 99 of 100 variables in the challenging frb100-40 benchmark instance.
Cite
@article{arxiv.1411.7480,
title = {Unweighted Stochastic Local Search can be Effective for Random CSP Benchmarks},
author = {Christopher D. Rosin},
journal= {arXiv preprint arXiv:1411.7480},
year = {2014}
}