Abstract: Two MIMO detecting algorithms corresponding to particle swarm optimization (PSO)based and genetic algorithm (GA)based detecting algorithms are designed. A novel genetic particle swarm optimization (GPSO) evolutionary method is proposed and applied to address the MIMO detecting problem. The proposed algorithm starts from improving the initial population, and divide the entire population into three types:elite particles, better particles and worst particles. Three different strategies of optimum value permutation, PSO evolvement and elimination strategy are employed corresponding to these three type particles to improve the local and global search ability. Therefore, both the optimum searching ability and the convergence speed are accelerated. Simulation results reveal that GPSO-based detecting algorithm takes much less size and less iteration number when compared with the PSO-based and the GA-based detecting method. Besides, compared with optimal maximum likehood(ML) detecting method, the GPSO-based detecting algorithm can reach better balance between the BER performance and the computational complexity.