The compact genetic algorithm
The compact genetic algorithm
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DOI：
10.1109/4235.797971
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发表时间：
1999-11-01
影响因子：
14.3
通讯作者：
Goldberg, DE
中科院分区：
文献类型：
--
作者：
Harik, GR;Lobo, FG;Goldberg, DE
本文介绍了紧凑型遗传算法（cGA），它代表人口的概率分布的解决方案，并在操作上相当于一阶行为的简单遗传算法与均匀交叉。它独立处理每个基因，比简单的GA需要更少的内存。紧凑的GA的发展是由正确理解GA的参数和运营商的作用。该文件清楚地说明了映射的简单GA的参数到一个等价的紧凑GA;计算机模拟比较两种算法的解决方案的质量和速度。最后，这项工作提出了重要的问题，在遗传算法中使用的信息，其后果向我们展示了一个方向，可以导致更有效的GA的设计。
This paper introduces the compact genetic algorithm (cGA) which represents the population as a probability distribution over the set of solutions and is operationally equivalent to the order-one behavior of the simple GA with uniform crossover. It processes each gene independently and requires less memory than the simple GA. The development of the compact GA is guided by a proper understanding of the role of the GA's parameters and operators. The paper clearly illustrates the mapping of the simple GA's parameters into those of an equivalent compact GA; Computer simulations compare both algorithms in terms of solution quality and speed.Finally, this work raises important questions about the use of information in a genetic algorithm, and its ramifications show us a direction that can lead to the design of more efficient GA's.