An Adaptive Immune Genetic Algorithm Based on Chaos Theory

The adaptive Immune genetic algorithm´s evolution speed is quick and the optimizing ability is unyielding, it´s a effective improvement of the standard genetic algorithm. But it still has the problem of easily falling into local optimum solution. The chaotic algorithm can carry out the ergodic character making a perturbation motion in a certain range of the value, so that the algorithm can jump out of local optimum solution, find the global parameter optimization. In this paper we lead chaos factor into the adaptive Immune algorithm to make the algorithm easier to find the global optimum solution.