A hybrid differential genetic algorithm for the Bouc-Wen hysteresis model is presented. The algorithm can adaptively adjust the scaling factor to change the value of the crossover probability factor. Similarly, it is can also automatically adjust the crossover probability factor to change the value of the scaling factor. Through the combination of the scaling factor and the cross factor, it can keep the individuals diversity and improve the searching ability of global optimum in the population at initial generations, as to quickly find the optimal model parameters. Moreover, the search ability of local optimal values can be improved, and the accuracy of the optimal model parameters can be further improved at a later time. The tranditional adaptive differential evolution algorithm is applied to the same Bouc-Wen hysteresis model. The simulation results show that the proposed algorithm not only has faster convergence speed but also has higher computational accuracy.