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Real-coded genetic algorithm (RGA) usuaUy meets the demand of consecutive space problem. However, compared with simple genetic algorithm (SGA), RGA also has the inherent disadvantages such as prematurity and slow convergence when the solution is close to the optimum solution. This paper presents an improved real-coded genetic algorithm to increase the computation efficiency and avoid prematurity, especially in the optimization of multi-modal function. In this method, mutation operation and crossover operation are improved. Examples are given to demonstrate its computation efficiency and robustness.