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Mar. 25, 2009 to Mar. 27, 2009
ISBN: 978-0-7695-3593-7
pp: 77-82
The satisfiability is a decision problem that belongs toNP-complete class and has significant applications invarious areas of computer science. Several works haveproposed high-performance algorithms and solvers toexplore the space of variables and look for satisfyingassignments. Pedrycz, Succi and Shai [1] have studieda fuzzy-genetic approach which demonstrates that aformula of variables can be satisfiable by assigningBoolean variables to partial true values between 0 and1. In this paper we improve this approach by proposingan improved fuzzy-genetic algorithm to avoidundesired convergence of variables to 0.5. Thealgorithm includes a repairing function that eliminatesthe recursion and maintains a reasonable computationalconvergence and adaptable population generation.Implementation and experimental results demonstratethe enhancement of solving satisfiability problems.
Satisfiability, Genetic Algorithms, fuzzy logic, NP-Completeness, Evolutionary Computation
Youakim Badr, José Francisco Saray Villamizar, "An Enhanced Fuzzy-Genetic Algorithm to Solve Satisfiability Problems", UKSIM, 2009, Computer Modeling and Simulation, International Conference on, Computer Modeling and Simulation, International Conference on 2009, pp. 77-82, doi:10.1109/UKSIM.2009.106
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