摘要
遗传算法是模拟达尔文的遗传选择和自然淘汰的生物进化过程的搜索最优解方法。作为一种新的全局优化搜索算法,遗传算法以其简单通用、适于并行处理以及高效、实用等显著特点,在各个领域得到了广泛应用。遗传算子的实现方法如选择算子、交叉算子和变异算子以及评估函数、适应度函数的设定方法等是遗传算法的重要组成部分,运用遗传算法可以进行问题求解,从而可以运用遗传算法解决聚类问题。因此,在图像处理、自动控制等方面可以充分利用遗传算法有效地解决图像聚类设计问题。
Genetic algorithm is researching the optimal resolution which is learned from Darwin＇ s genetic choose and natural elimination during the process of biology improvement. Being a new and whole better re- search algorithm, genetic algorithm is widely used in all kinds of fields, because it has outstanding features such as single wide use, good to parallel process, efficiency and well use et al. The finish technique including choo- sing algorithm, cross algorithm, variety algorithm and the designing technique of assess function, apply function become the very important parts of genetic algorithm. Genetic algorithm can both resolve problem and resolve clustering problem. Therefore, in some domain such as image processing, automation control, the image cluste-ring problem with taking full advantage of genetic algorithm is resolved effectively.
出处
《测控技术》
CSCD
北大核心
2010年第2期44-46,51,共4页
Measurement & Control Technology
关键词
图像聚类设计
遗传算法
解码
评估函数
image clustering design
genetic algorithm
decode
assess function
