Weight Redistribution for Unweighted MAX-SAT
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Many real-world problems are over-constrained and require search techniques adapted to optimising cost functions rather than search- ing for consistency. This makes the MAX-SAT problem an important area of research for the satisility (SAT) community. In this study we perform an empirical analysis of several of the best performing SAT local search techniques in the domain of unweighted MAX-SAT. In particular, we test two of the most recently developed SAT clause weight redistri- bution algorithms, DDFW and DDFW+, against three more well-known techniques (RSAPS, AdaptNovelty+ and PAWS). Based on an empir- ical study across a range of previously studied problems we conclude that DDFW is the most promising algorithm in terms of robust average performance.
AI 2007: Advances in Artificial Intelligence
Copyright 2007 Springer. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.com.