An Optimized Genetic Algorithm for TSP

被引：0
|
作者：
Bai, Dongling [1 ]
Guo, Qingping [1 ]
机构：
[1] Wuhan Univ Technol, Dept Comp Sci & Technol 1, Wuhan 430063, Hubei, Peoples R China
来源：
DCABES 2008 PROCEEDINGS, VOLS I AND II | 2008年
关键词：
Genetic Algorithm; Selection; Crossover; Mutation; TSP;
D O I：
暂无
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
It is difficult to find out a precise answer to TSP (Traveling Salesman Problem). But GA (Genetic Algorithm) can find a better answer to it. This paper introduce GA and its essence. Then TSP, which is based on GA, is presented. In this paper main operators in GA, such as crossover operators and mutation operators, are analyzed and compared. By employing. heuristic crossover and inversion mutation, a new method based on genetic algorithm for solving TSP is presented. The experimental results simulated on TSP show that this algorithm is feasible and effective to solve. TSP. Employing heuristic crossover and inversion mutation can prevent premature convergence and ensure that the population is diverse, like what happens in the nature.
引用
收藏
页码：667 / 671
页数：5
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