This study is concerned with the Boolean satisfiability ISAT) problem and its solution in setting a hybrid computational intelligence environment of genetic and fuzzy computing. In this framework, FUZZY sets realize an embedding principle meaning that original two-valued (Boolean) functions under investigation are extended to their continuous countelparts resulting in the form of fuzzy (multivalued) functions. In the sequel, the SAT problem is refonnulated for the fuzzy functions and solved using a genetic algorithm (GA). It is shown that a GA, especially its recursive version, is an efficient tool for handling multivariable SAT problems. Thorough experiments revealed that the recursive version of the GA can solve SAT problems with more than 1000 variables.
Pedrycz W, Succi G, Shai O (2002). Genetic-Fuzzy Approach to the Boolean Satisfiability Problem. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 6(5), 519-525.
Genetic-Fuzzy Approach to the Boolean Satisfiability Problem
Succi G;
2002
Abstract
This study is concerned with the Boolean satisfiability ISAT) problem and its solution in setting a hybrid computational intelligence environment of genetic and fuzzy computing. In this framework, FUZZY sets realize an embedding principle meaning that original two-valued (Boolean) functions under investigation are extended to their continuous countelparts resulting in the form of fuzzy (multivalued) functions. In the sequel, the SAT problem is refonnulated for the fuzzy functions and solved using a genetic algorithm (GA). It is shown that a GA, especially its recursive version, is an efficient tool for handling multivariable SAT problems. Thorough experiments revealed that the recursive version of the GA can solve SAT problems with more than 1000 variables.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
