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• W2090854154 abstract "A large number of problems that occur in knowledge-representation, learning, very large scale integration technology (VLSI-design), and other areas of artificial intelligence, are essentially satisfiability problems. The satisfiability problem refers to the task of finding a satisfying assignment that makes a Boolean expression evaluate to True. The growing need for more efficient and scalable algorithms has led to the development of a large number of SAT solvers. This paper reports the first approach that combines finite learning automata with the greedy satisfiability algorithm (GSAT). In brief, we introduce a new algorithm that integrates finite learning automata and traditional GSAT used with random walk. Furthermore, we present a detailed comparative analysis of the new algorithm's performance, using a benchmark set containing randomized and structured problems from various domains." @default.
• W2090854154 created "2016-06-24" @default.
• W2090854154 creator A5071922620 @default.
• W2090854154 creator A5084813055 @default.
• W2090854154 date "2010-08-01" @default.
• W2090854154 modified "2023-09-27" @default.
• W2090854154 title "Combining finite learning automata with GSAT for the satisfiability problem" @default.
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• W2090854154 doi "https://doi.org/10.1016/j.engappai.2010.01.009" @default.
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