一种几何图形目标的检测之遗传算法

提出一种遗传算法用来检测图像中的几何图形，将待检测几何图形的参数，采用浮点数编码。参照经典的HOCGH变换，构造个体的适应度函数，采用最优保存策略，将群体中适应度最高几位的个体，直接复制到下一代群体中，其余的个体，使用多点交叉运算和均匀变异运算，形成新的个体，遗传到下一代群体中，当满足算法结束条件时，将群体中适应度最高的个体经解码后，作为检测出来的几何图形的参数。本文提出的遗传算法，能够消除噪声干扰，收敛性好，计算结果精确，与经典的HOUGH变换相比，本算法时间和存储空间开销小，易于计算机编程的实现。

A genetic algorithm for detecting target geometric figures.

A genetic algorithm is proposed to detect target geometric figures in an given image. Float-point encoding was adopted to process the parameters of a geometric figures to be detected. On the basis of classical Hough transform, a fitness function was obtained for each individual task, and the individuals with the highest fitness function were identified and copied into the cohort of the next generation. For the rest of the individuals, operation with multi-point crossover or uniform mutation was performed to form new individuals in the next generation. When the termination conditions for this genetic algorithm were met, the best-fitted individual was decoded and output as the parameters of the detected geometric figures. This algorithm can eliminate noise interference with good convergence and accurate results, and may save time and storage space during relatively easily programmed computation in comparison with classical Hough transform.