Choosing the best parameter setting is a well-known important and challenging task in evolutionary algorithms (EAs). As one of the earliest parameter tuning techniques, the meta-EA approach regards each parameter as a variable and the performance of algorithm as the fitness value and conducts searching on this landscape using various genetic operators. However, there are some inherent issues in this method. For example, some algorithm parameters are generally not searchable because it is difficult to define any sensible distance metric on them. In this paper, a novel approach is proposed by combining the meta-EA approach with a method called racing, which is based on the statistical analysis of algorithm performance with different parameter settings. A series of experiments are conducted to show the reliability and efficiency of this hybrid approach in tuning genetic algorithms (GAs) on two benchmark problems.