An Adaptive Genetic Algorithm for Solving Traveling Salesman Problem

被引：0
|
作者：
Wang, Jina [1 ]
Huang, Jian [1 ]
Rao, Shuqin [1 ]
Xue, Shaoe [1 ]
Yin, Jian [1 ]
机构：
[1] Sun Yat Sen Univ, Dept Comp Sci, Guangzhou 510275, Guangdong, Peoples R China
来源：
ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS, PROCEEDINGS: WITH ASPECTS OF ARTIFICIAL INTELLIGENCE | 2008年 / 5227卷
关键词：
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：
Traveling salesman problem (TSP) is a classical NP-hard problem in combinational optimization. This paper adopted a novel genetic algorithm which adjust the crossover probability and mutation probability adaptively based on clustering and fuzzy system, and designed a new crossover operator to improve the performance of genetic algorithm (GA) for TSP. Experiments show that the proposed method is much better than the standard genetic algorithm with a higher convergent rate and success rate.
引用
收藏
页码：182 / 189
页数：8
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