@inProceedings{
 title = {An enhanced fuzzy-genetic algorithm to solve satisfiability problems},
 type = {inProceedings},
 year = {2009},
 identifiers = {[object Object]},
 keywords = {Evolutionary computation,Fuzzy logic,Genetic algorithms,NP-completeness,Satisfiability},
 id = {46dc781a-9e43-314b-a99a-20c9558fc405},
 created = {2018-02-20T21:30:51.909Z},
 file_attached = {false},
 profile_id = {54318c42-96bc-3aaf-a27d-ca643ea40072},
 last_modified = {2018-02-20T21:30:51.909Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {The satisfiability is a decision problem that belongs to NP-complete class and has significant applications in various areas of computer science. Several works have proposed high-performance algorithms and solvers to explore the space of variables and look for satisfying assignments. Pedrycz, Succi and Shai [1]  have studied a fuzzy-genetic approach which demonstrates that a formula of variables can be satisfiable by assigning Boolean variables to partial true values between 0 and 1. In this paper we improve this approach by proposing an improved fuzzy-genetic algorithm to avoid undesired convergence of variables to 0.5. The algorithm includes a repairing function that eliminates the recursion and maintains a reasonable computational convergence and adaptable population generation. Implementation and experimental results demonstrate the enhancement of solving satisfiability problems. © 2009 IEEE.},
 bibtype = {inProceedings},
 author = {Villamizar, J.F.S. and Badr, Y. and Abraham, A.},
 booktitle = {11th International Conference on Computer Modelling and Simulation, UKSim 2009}
}

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