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We present ULSA, a novel stochastic local search algorithm for random binaryconstraint satisfaction problems (CSP). ULSA is many times faster than theprior state of the art on a widely-studied suite of random CSP benchmarks.Unlike the best previous methods for these benchmarks, ULSA is a simpleunweighted method that does not require dynamic adaptation of weights orpenalties. ULSA obtains new record best solutions satisfying 99 of 100variables in the challenging frb100-40 benchmark instance.

Unweighted Stochastic Local Search can be Effective for Random CSP Benchmarks