An adaptive nonlinear genetic algorithm for numerical optimization

被引：0

作者：

Cui, ZH ^{[1
]}

Zeng, JC ^{[1
]}

机构：

[1] Taiyuan Heavy Machinery Inst, Div Syst Simulat & Comp Applicat, Shanxi 030024, Peoples R China

来源：

2002 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-4, PROCEEDINGS | 2002年

关键词：

nonlinear genetic algorithm; numerical optimization; nonlinear genetic operators; genetic algorithm;

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：

Through mechanism analysis of simple genetic algorithm (SGA), we find that every genetic operator can be considered as a linear transform. So some disadvantages of SGA may be solved if genetic operators are modified to nonlinear transform. According to the above method, nonlinear genetic algorithm is introduced, and different nonlinear genetic operators with some probabilities are designed and applied to numerical optimization problems. The optimization computing of some examples is made to show that the new genetic algorithm is useful and simple.

引用

页码：1559 / 1561

页数：3

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[2]

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[3]

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