arXiv Open Access 2014

Unweighted Stochastic Local Search can be Effective for Random CSP Benchmarks

Christopher D. Rosin

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Abstrak

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.

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cs.AI

Penulis (1)

Christopher D. Rosin

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APA MLA BibTeX

Rosin, C.D. (2014). Unweighted Stochastic Local Search can be Effective for Random CSP Benchmarks. https://arxiv.org/abs/1411.7480

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Informasi Jurnal

Tahun Terbit: 2014
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓