An equivalent genetic algorithm based on extended strings and its convergence analysis

被引：9
|
作者：
Liang, YC
Zhou, CG
Wang, ZS
Lee, HP
Lim, SP
机构：
[1] Jilin Univ, Dept Math, Changchun 130012, Peoples R China
[2] Jilin Univ, Dept Comp Sci, Changchun 130012, Peoples R China
[3] Natl Univ Singapore, Dept Mech Engn, Ctr Adv Computat Engn Sci, Singapore 119260, Singapore
来源：
INFORMATION SCIENCES | 2001年 / 138卷 / 1-4期
基金：
中国国家自然科学基金;
关键词：
extended-string-based genetic algorithm (ESGA); convergence analysis; rate of convergence; global optimal solution;
D O I：
10.1016/S0020-0255(01)00122-0
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A novel equivalent genetic algorithin based on extended strings (ESGA) is presented in this paper. The ESGA is equivalent to the canonical genetic algorithms (CGAs) in optimization problems, whereas the manner of the group by group implementation makes it different from the conventional genetic algorithms. In this paper the conditions and the rate of convergence of the ESGA are analyzed theoretically. It is shown that the ESGA can converge to the global optimal solution and the average value of the times of reaching the optimal point is finite. An analytical expression for the average rate of convergence of the ESGA is also provided. (C) 2001 Elsevier Science Inc. All rights reserved.
引用
收藏
页码：119 / 135
页数：17
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