An evolutionary algorithm with population immunity and its application on autonomous robot control

The natural immune system is an important resource full of inspiration for the theory researchers and the engineering developers to design some powerful information processing methods aiming at difficult problems. Based on this consideration, a novel optimalsearching algorithm, the immune mechanism based evolutionary algorithm-IMEA, is proposed for the purpose of finding an optimal/quasi-optimal solution in a multi-dimension space. Different from the ordinary evolutionary algorithms, on one hand, due to the long-term memory, IMEA has a better capability of learning from its experience, and on the other hand, with the clonal selection, it is able to keep from the premature convergence of population. With the simulation on autonomous robot control, it is proved that IMEA is good at the task of adaptive adjustment (offline), and it can improve the robot's capability of reinforcement learning, so as to make itself able to sense its around dynamic environment.