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means the range of over . One of the original idea to introduce the interval function is to evaluate the range of the value of the function when the variable changes in the range of in a statement of interval way. However, because only some of the algebraic laws which valid for real numbers hold only in a weaker form for interval numbers, the computational results of depends on the calculating order severely, and they are often larger than the value of . A number of literatures took efforts on finding the skills to obtain the better results of . And some valuable rules were found. For example, (1) If each variable , , occurs at most once in , then ; (2) To make the most of the subdistributivity, i.e., to execute the addition and subtraction operation first, to execute multiplication and division operations then. For instance, the better result of the polynomial can be obtained through computing its reformed form . However, the similar results to improve the results of a rational function have not been found. In fact, the best result of can be obtained through calculate directly. The bounds of can be obtained through solving the following two optimization problems. (8) (9) It is clear that the optima indicated in optimization problem (8) and optimization problem (9) refer to the global optima of in . When is monotone in , there are only one local maximum and one local minimum of , they are the global maximum and the global minimum of in respectively. Many optimization algorithms (for example, Newton algorithm, Nowton-Raphson algorithm, Gauss Newton algorithm, etc) that only can locate the local optima of the problems could be used. However, when the expression is not monotonic in , or the monotone property is unknown, the global optimization method that has good capability to locate the global optima of in is needed. In this work, a real-code genetic algorithm is used to locate the global optima of optimization problem (8) and (9). It can be briefly described as: (1) The fitness function of individuals is defined by and for minimum and maximum problems respectively. and respectively are the maximum and the minimum of in he generations up to now. The Goldberg’s linear scaling formulation ( ) is used also for fitness scaling [9]. (2) The proportional selection model was used. (3) The arithmetic crossover operator [10] was used. (4) Both of the non-uniform mutation operator [10] and the boundary mutation operator [10] were used in this study. (5) The elitist strategy was used to add the best individual in the previous population to the next generation, in place of its worst individual. (6) A maximum number of generations is specified for stopping the evaluation. (3) Mathematic examples to examine the presented approach Example 1. Consider the polynomial function in the interval =[-5,5]. Fig. 2 the graph of f(x) The figure of was showed in Fig.2. It is to see from Fig.2 that is not monotonic in [-5, 5]. The global minimum and the global maximum value of in are and respectively. Whence . The accurate result could be easily obtained by using the present method. However, the solution was obtained by directly using the interval arithmetic operations. A better result, , was obtained by using the reformed form . Numerical examples The nominal parameters for M