This study investigates applying genetic algorithms (GAs) to solve fuzzy equations without defining membership functions for fuzzy numbers, neither using the extension principle and interval arithmetic and alpha-cut operations, nor using a penalty method for constraint violations. Three experimental examples were employed to illustrate the effectiveness of the proposed GA approach in solving fuzzy equations with constraints. An essential issue of applying GAs for obtaining better solution to the problem is the parameter settings including the probability of crossover, the probability of mutation, and the number of generations. Experimental results show that the proposed GA approach obtains very good solutions within the given bounds of each uncertain variable in the problems. The fuzzy concept of the GA approach is different, but provides better solutions than classical fuzzy methods.
