The dual-band predistortion models suffer from high complexity and non-adaptability of optimization algorithms. To address this issue

this paper proposes an adaptive optimization algorithm for dual-band predistortion model with reduced complexity. We use dual-band general memory polynomial (DB-GMP) as the predistortion model where all basis function terms of the original DB-GMP model are sorted by orthogonal matching pursuit algorithm. In each iteration

all selected basis function terms help to construct an alternative model. We then derive the Bayesian information criterion (BIC) when output vector elements of the DB-GMP model are with non-independent identical distributions

and the model with smallest BIC value is treated as the optimized model. Finally

we achieve the proposed algorithm without the information of model sparsity and fitting error threshold. Simulation results show that compared with the original DB-GMP model

the coefficient number of the optimized model is reduced by more than 75%

while both the models after predistortion have almost the same level of adjacent channel power ratio and normalized mean squared error

leading to good predistortion performance.