Thresholding image segmentation based on the cloud-model-based genetic algorithm was proposed. The algorithm selection put the best threshold value image segmentation problem is converted into the optimization problem of genetic algorithm. According to the characteristics of normal membership cloud expected curve equation

the cloud model is introduced into the genetic algorithm. The X-conditional cloud generator for the normal cloud model is used as cross probability and mutation probability in this hybrid genetic algorithm

Avoid the genetic algorithms falling into local optimization. The experimental results show that the range of the thresholds is more stable than traditional genetic algorithm and it less time consuming and better satisfies the request of real-time processing in image segmentation.