结合 3种点群目标选取的一般原则和遗传算法的基本原理与特点，设计了基于遗传算法的点群目标选取模型.考虑到要最大限度地保持点群的分布范围、排列规律、内部各地段的分布密度等因素，基于遗传算法的点群选取模型的基本原理是 :首先采用自适应分类方法，将点群 M依照密度分成若干类子点群，然后根据每个子点群的点数和最后要保留的总的点数，计算每个子点群中要保留的点数，最后结合凸壳化简方法和遗传算法对点进行选择.在对关键性步骤进行讨论的基础上，本文针对某一地区的点群目标分别采用基于遗传算法的点目标选取方法与凸壳选取方法进行了选取对比实验.从实验结果和遗传算法的特点分析可以看出，基于遗传算法的点目标选取方法的特点是非常明显的，其适用于分散式居民地记号房、可看作点状目标的小湖泊群等点状要素的选取 ;能够保持密度分布特征及其排列规律 ;外围轮廓特点没有大的改变

Combining the basic principle and characteristics of genetic algorithms with the there basic principles of point cluster selection, which are the selection according to the standard of selection, the selection according to the important meanings, the selection according to the range of distributing and density, we design a model of point cluster selection based on genetic algorithms. Considering that we must do our best to preserve the range of distributing , the principle of arranging, the density of distributing of point cluster, the basic principle of the model of point cluster selection is that divide the point cluster M into a sub-clusters: A 1, A 2,...,A a according to the density, then calculate the preserved amount of every sub-cluster according to the amount of every sub-cluster and the total amount to be preserve, last combine convex hull with genetic algorithms to select. The results of the experiments are compared, one is by the methods of the selection based on genetic algorithms and the other is by the methods of the selection of convex hull. From the experimental output, we can get there conclusions: (1) The model of point cluster selection based on genetic algorithms can be better in the selection of dispersive object. (2) The model can preserve the characteristics of the density and the principles of arrangement well. (3) The model can preserve the outlook of the cluster well.