To improve the performance of genetic programming algorithm, the niche technology used in genetic algorithm is applied to genetic programming. It is improvement of genetic programming algorithm, which is called NGP in the next text. First, the algorithm fits the data with the original training set. Second, it tracks the extreme points of the fitting function, and according to the dimensions of the fitting function, calculate the extreme points' Euclidean distance in the independent variable dimension and order it. Then it selects the extreme points whose Euclidean distance is larger and does not exceed the number of the ten percent of the original training set, and added them to the original training set as new training set. Finally, it deals with new training set using genetic programming. In this paper, we use symbolic regression experiment to test the accuracy of the NGP. It illustrates the accuracy and effectiveness of the algorithm.%为了提高遗传规划算法的性能,把遗传算法中的小生境技术运用到遗传规划中,提出了改进的遗传规划算法(NGP).该算法首先对原始训练集进行数据拟合,然后应用小生境技术跟踪拟合函数的极值点,并根据拟合函数的维数的不同,分别计算极值点在自变量维上的欧氏距离并排序,选取欧式距离较大且数量不超过原始训练集10%的极值点,加入到原始训练集中作为新的训练集,最后用遗传规划算法处理新训练集.在符号回归实验中对NGP的准确率进行了测试,说明了该算法的准确性和有效性.
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