Abstract: The researches on neural network ignored the closely related connection between its architecture and the weighted value for years, but mainly focused on their optimization. A neural evolution algorithm based on immune-evolution strategy is therefore proposed introducing the concentration/memory mechanism of immune system into evolutionary strategy, which can optimize simultaneously both the topological structure of network and weighted value for connection. Furthermore, the algorithm introduces the Cauchy operator instead of Gauss operator to obtain better global convergence. Theoretical analysis and simulation results both show that the algorithm can retain the population diversity, and avoid premature convergence with better global convergence as well as the ability to learn faster to learn the architecture and the weighted value of network.