Comparison of steady state and elitist selection genetic algorithms

被引：0

作者：

Shi, Z ^{[1
]}

Cui, ZL ^{[1
]}

Zhou, Y ^{[1
]}

机构：

[1] Harbin Inst Technol, Sch Mech & Elect Engn, Harbin 150001, Peoples R China

来源：

PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON INTELLIGENT MECHATRONICS AND AUTOMATION | 2004年

关键词：

steady state genetic algorithm; elitist selection genetic algorithm; optimization;

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：

It is proposed that the comparison problem of two models of the genetic algorithm, the steady state genetic algorithm and the elitist selection genetic algorithm. The convergence speed, on-line and off-line performance of the two algorithms in different environments are compared. It is experimentally shown that the steady state genetic algorithm is a simple, effective genetic algorithm. The steady state genetic algorithm runs well in low-dimensional environment especially its good on-line performance. On the other band the elitist selection genetic algorithm runs well in high-dimensional environment, it has good capability in searching optimal value in a big space. The difference between the searching ability of two algorithms was explained by the theory of Implicit Parallelism. The difference between the two algorithms on-line performances was explained by the difference ways of reproduction the two models used.

引用

页码：495 / 499

页数：5

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