The fuzzy genetic algorithm based on rule

Fuzzy numbers are most commonly used in tackling optimization problems in many practical domains. Rationally ranking fuzzy information becomes the basis for solving uncertainty optimization problems. Due to the credibility is not embodied by the order relation derived by level cut intervals or the order relation determined by centralized quantifying, so it is difficult to use them to rank fuzzy information under certain consciousness for fuzzy rules. In view of this, we introduce the concept of location values of fuzzy numbers with rule pools, and then develop a new way of ranking fuzzy numbers. Moreover, we give further descriptions for the location values by using coincidence degrees of fuzzy numbers with rule pools, and present the technique of composite quantification for fuzzy information. Furthermore, we establish a numerical model for computing location values and coincidence degrees. Finally, by using the location values and coincidence degrees of fuzzy numbers with rules, we propose a fuzzy genetic algorithm based on rules (BR-FGA), and then illustrate it by using two examples.