A novel fast convergent genetic algorithms using adaptive techniques

被引：0

作者：

Liu, De-Peng ^{[1
]}

机构：

[1] Hangzhou Dianzi Univ, Sch Sci, Hangzhou 310018, Zhejiang, Peoples R China

来源：

Proceedings of 2006 International Conference on Machine Learning and Cybernetics, Vols 1-7 | 2006年

关键词：

genetic algorithms; mutation; probability; adaptive;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents a modified genetic algorithms, which based on tuning of mutation probability by the value of individual fitness. The fine modular in current generation is easy to survive in the offspring, and at the same time the variety of population is also guaranteed. In modified scheme, the order of crossover and mutation is changed in order to avoid repeated computing of individual fitness. Simulation result shows that the modified scheme is prior to the GAs commonly used.

引用

页码：3416 / 3418

页数：3