New evolutionary genetic algorithms for China Travelling Salesman Problem

被引：0

作者：

Dang, JW ^{[1
]}

Jin, F ^{[1
]}

机构：

[1] Lanzhou Railway Univ, Coll Informat Engn, Lanzhou 730070, Peoples R China

来源：

8TH INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING, VOLS 1-3, PROCEEDING | 2001年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Evolutionary genetic algorithms have been proposed to NP-complete combinatorial optimization problem. A new crossover operator based on group theory has been created. Computational processes motivated by proposed evolutionary genetic algorithm were described. The proposed algorithms were used in solving China-Travelling Salesman Problem. The experimental results showed the superiority of new evolutionary algorithms in comparison with the other algorithms.

引用

页码：131 / 135

页数：5

共 6 条

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[2]

DANG JW, 2001, J LANZHOU RAILWAY U, V20

[3]

DANG JW, 2000, NEURAL NETWORK TECHN

[4]

FOGEL DB, 1998, EVOLUTIONARY APPROAC

[5]

Goldberg David E., 1985, P 1 INT C GENETIC AL, P154, DOI DOI 10.4324/9781315799674

[6] NEURONS WITH GRADED RESPONSE HAVE COLLECTIVE COMPUTATIONAL PROPERTIES LIKE THOSE OF 2-STATE NEURONS [J].

HOPFIELD, JJ .

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