Abstract
The satisfiability problem is the first problem proved to be NP-complete and has been one of the core NP-complete problems since then. It has many applications in many fields such as artificial intelligence, circuit design and VLSI testing. The maximum satisfiability problem is an optimization version of the satisfiablity problem, which is a decision problem. In this study, a hybrid genetic algorithm is proposed for the maximum satisfiability problem. The proposed algorithm has three characteristics: 1. A new fitness function is designed to guide the search more effectively; 2. A local search scheme is designed, in which a restart mechanism is devised to help the local search scheme escape from the solutions near an already searched local optimum; 3. The local search scheme is hybridized with a two-layered genetic algorithm. We compared the proposed algorithm with other algorithms published in the literature, on the benchmarks offered by Gotllieb, Marchion and Rossi [12] and Hao, Lardeux and Saubion[18].
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Tseng, LY., Lin, YA. (2013). A Hybrid Genetic Algorithm for the Maximum Satisfiability Problem. In: Ali, M., Bosse, T., Hindriks, K.V., Hoogendoorn, M., Jonker, C.M., Treur, J. (eds) Recent Trends in Applied Artificial Intelligence. IEA/AIE 2013. Lecture Notes in Computer Science(), vol 7906. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38577-3_12
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DOI: https://doi.org/10.1007/978-3-642-38577-3_12
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