Genetic Algorithm applied to optimization problems with fuzzy coefficient matrix

In general, conventional methods for solving fuzzy nonlinear programming problems are not practical; because in most cases objective function and constraints are not continuous and differentiable or the problem may be of large size. Recently, genetic algorithms are used to solve many real-world problems and have received a great deal of attention about their ability as optimization techniques for optimization problems. In this article, we consider fuzzy nonlinear optimization problems with linear constraints. we convert this type of problems into a crisp model and then we solved the crisp model with genetic algorithms. we can obtain the solution of the fuzzy form problem by use of ranking function method. The efficiency of the proposed method is shown by solving examples.