Abstract: Nonlocal crystal plasticity models account for the spatial rearrangement of dislocations due to non-uniform deformation, resulting in complex constitutive models with many adjustable parameters. Hence, it is challenging to determine these parameters accurately using the conventional trial-and-error method. Although genetic algorithms can solve the parameter identification problem robustly by global optimization, their computational costs are relatively high for nonlocal crystal plasticity models. To address this issue, this paper proposes a genetic algorithm coupled with a machine learning model to effectively reduce the computational cost. Focusing on the tensile response of nickel-based superalloys with cooling holes, the uniaxial tensile stress-strain curve is the objective, and an evaluation formula is established based on the yield stress and the final stress, to make the optimization result as close as possible to the experiment. Through this method, the machine learning model can predict the corresponding stress values based on the parameters of the nonlocal crystal plasticity model, thus replacing the finite element calculation that was originally required in the genetic algorithm. To analyze the influence of the constitutive model parameters on the uniaxial tensile mechanical response, the SHAP framework is adopted and verified by the finite element results. The results show that the nonlocal crystal plasticity model parameters can be determined effectively by this method, and the stress-strain responses calculated by the parameters agree well with the experimental results. Moreover, the SHAP framework can offer insights into the significance of the constitutive model parameters, as well as their influence on the yield stress and the ultimate stress.