| 遗传算法的研究与应用 |
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| 引用本文： | 徐清振,肖成林.遗传算法的研究与应用[J].现代计算机,2006,19(5):19-22. |
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| 作者姓名： | 徐清振 肖成林 |
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| 作者单位： | [1]华南师范大学计算机学院,广州510631 [2]华南理工大学应用数学系,广州510640 |
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| 基金项目： | 本课题得到国家自然科学基金（60273063）资助. |
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| 摘 要： | 根据遗传算法的一些基本概念以度遗传算法的操作流程。对遗传算法的繁殖算子从数学上给出定义，刻划了繁殖算子的本质。遗传算法是一种基于概率意义上的随机搜索算法。但它是从空间上的一组点而不是一个点出发。因此遗传算法的搜索能力比其他随机搜索算法更强，可以找到全局范围内的最优解。但是。应该注意在遗传算法的应用中，要避免其过早的收敛，防止陷入局部最优解。
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| 关 键 词： | 遗传算法 编码 选择（selection）算子 交叉算子 |
| 收稿时间： | 2006-03-16 |
| 修稿时间： | 2006-03-16 |
The Research and Application of Genetic Algorithm |
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| XU Qing-zhen,XIAO Cheng-lin.The Research and Application of Genetic Algorithm[J].Modem Computer,2006,19(5):19-22. |
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| Authors: | XU Qing-zhen XIAO Cheng-lin |
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| Affiliation: | 1. School of Computer Science, South China Normal University, Guangzhou 510631 China; 2. Apply Mathematics of South China University of Technology, Guangzhou 510640 China |
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| Abstract: | We define the reproduction operators of genetic algorithms by mathematics, and give out the essence of reproduction operators. Genetic algorithms are random search algorithms based on probability, however they start from a set of points in space not from a point, the search ahihty of genetic algorithms is better than that of other random search algorithms, and it can get optimized solutions in a whole range. But we should pay attention to avoid convergence earlier and be in local optimized solution in the application of genetic algorithm, |
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| Keywords: | Genetic Algorithm Coding Selection Operator Crossover Operator |
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