The TSP is a typical NP problem. The optimization of vehicle routing problem (VRP) and city pipeline optimization can use TSP to solve; therefore it is very important to the optimization for solving TSP problem. The genetic algorithm (GA) is one of ideal methods in solving it. The standard genetic algorithm has some limitations. Improving the selection operator of genetic algorithm, and importing elite retention strategy can ensure the select operation of quality, In mutation operation, using the adaptive algorithm selection can improve the quality of search results and variation, after the chromosome evolved one-way evolution reverse operation is added which can make the offspring inherit gene of parental quality improvement opportunities, and improve the ability of searching the optimal solution algorithm.

1.
Cheng-Hong
Yang
,
Sin-Hua
Moi
,
Yu-Da
Lin
,
Li-Yeh
Chuang
.
Genetic Algorithm Combined with a Local Search Method for Identifying Susceptibility Genes
[J].
Journal of Artificial Intelligence and Soft Computing Research
,
2016
,
6
(
3
).
2.
Kazi Shah Nawaz
Ripon
,
Sam
Kwong
,
K.F.
Man
.
A real-coding jumping gene genetic algorithm (RJGGA) for multiobjective optimization
[J].
Information Sciences
,
2006
,
177
(
2
).
3.
Manojkumar
Ramteke
,
Nitish
Ghune
,
Vibhu
Trivedi
.
Simulated binary jumping gene: A step towards enhancing the performance of real-coded genetic algorithm
[J].
Information Sciences
,
2015
,
325
.
4.
Razib M.
Othman
,
Safaai
Deris
,
Rosli M.
Illias
.
A genetic similarity algorithm for searching the Gene Ontology terms and annotating anonymous protein sequences
[J].
Journal of Biomedical Informatics
,
2007
,
41
(
1
).
5.
Shital
Shah
,
Andrew
Kusiak
.
Cancer gene search with data-mining and genetic algorithms
[J].
Computers in Biology and Medicine
,
2006
,
37
(
2
).
6.
Jayanthi
Manicassamy
,
P.
Dhavachelvan
.
Gene Trans infection Directs towards Gene Functional Enhancement Using Genetic Algorithm
[J].
IERI Procedia
,
2013
,
4
.
7.
Zhang
Hao
.
Mixed Recommendation Algorithm Based on Commodity Gene and Genetic Algorithm
[M].
Springer
London
:
2013
-06-15.
8.
Jayanthi
Manicassamy
,
S. Sampath
Kumar
,
Mohana
Rangan
,
V.
Ananth
,
T.
Vengattaraman
,
P.
Dhavachelvan
.
Gene Suppressor: An added phase toward solving large scale optimization problems in genetic algorithm
[J].
Applied Soft Computing
,
2015
,
35
.
9.
Edmundo Bonilla
Huerta
,
Beatrice
Duval
,
Jin-Kao
Hao
.
Gene Selection for Microarray Data by a LDA-Based Genetic Algorithm [M]
.
Springer
Berlin Heidelberg
:
2008
-06-15.
10.
Topon Kumar
Paul
,
Hitoshi
Iba
.
Gene selection for classification of cancers using probabilistic model building genetic algorithm
[J].
Bio Systems
,
2005
,
82
(
3
).
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