Abstract: Simple genetic algorithm has a slow convergence velocity in late evolution and gets premature con-vergence easily.To solve these problems,an immune learning based genetic algorithm(ILGA) is proposed.The key to the algorithm lies in maintaining diversity of the population and executing the strategy of reinforcement learning and immature protection.The algorithm not only keeps the leading position of excellent antibody,but also develops the potential of rapidly growing antibody in seeking optimum.Under the action of excellent memory cell,the search of algorithm to global optimum is rapid and effective.Simulation results show that the algorithm has better global con-vergence ability and rapider convergence velocity through optimization tests of benchmark functions.The T-S fuzzy neural network controller is optimized by ILGA on a double inverted pendulum.Experiment results demonstrate that the method has ideal steady performance and quick response speed.