基于标准差的自适应激素调节遗传算法

Adaptive Genetic Algorithm Based on Hormone Regulation

基于生物内分泌系统的激素调节原理,提出了一种新的自适应遗传算法。该算法以内分泌激素调节的H ill函数下降形式为基础,设计了自适应交叉算子和自适应变异算子,使交叉率和变异率在遗传算法迭代过程中,能够根据函数适应度值的标准差进行自适应调节,使得整个进化过程中将种群多样性维持在合理水平,从而保证算法的正常进化。4种测试函数及三维人脑图像分割的实验结果显示,提出的自适应遗传算法可较好地保持种群多样性并克服早熟现象,性能优于其他3种自适应遗传算法及传统遗传算法。

An improved adaptive genetic algorithm is proposed based on the principle of hormone modulation in endocrine system. An adaptive crossover operator and an adaptive mutation operator based on the downward form of Hill function are designed in the algorithm. The crossover rate and mutation rate are made self-regulated according to the standard deviation of fitness value in each generation. And the diversity is maintained at a reasonable level in the whole process of evolution to ensure the normal evolution of genetic algorithm. Experimental results of four test functions and 3D brain image segmentation show the improved genetic algorithm can maintain the diversity of the population effectively, and overcome the premature problem. The performances of the algorithm are better than those of the other three adaptive genetic algorithms and the traditional genetic algorithm.