基于遗传算法的多边形逼近3D数字曲线

Polygonal Approximation of 3D Digitized Curves Using Genetic Algorithms

首先对 3D数字曲线进行简单的数据压缩 通过对该曲线上的点列进行二进制编码定义来表示数字曲线的染色体 二进制串中的每一个位称为基因 ,每一个逼近多边形和染色体形成 1 1映射 目标函数使给定曲线和逼近多边形之间的均方差最小 构造了解决该问题的选择、交叉、变异三个算子 所得最优染色体中基因值为 1的基因对应数字曲线的分界点 实验结果表明 。

A simple data reduction is first applied to the digitized curve. Chromosomes are defined by encoding the point sequence into binary strings to represent the digitized curve. Each bit of binary strings is called a gene. Each polygonal approximation is mapped to a unique binary string. The objection function is defined as the mean square errors between the given digitized curve and the approximated polygonal. Three genetic operators, namely selection, crossover and mutation, are constructed to solve the problem. Points of 3D digitized curve corresponding to genes of a chromosome, equal to 1 s, are demarcation ones. Experimental results show that this approach can get more accurate result of approximation.