An Improved Genetic Algorithm based on Immune Theory in Target Segmentation of Infrared Images

In this paper, an improved genetic algorithm based on immune thought is proposed. The adaptive ability of the immune system is used to improve the optimization ability of the genetic algorithm. The immune genetic algorithm is divided into antigen memory, antibody encouragement and restraint, antibody diversity-keeping and other processes, and is used for infrared image segmentation. Compared with many classical segmentation methods, the immune genetic algorithm proposed in this paper has better effect and more advantageous performance indexes.