Dynamic circle nonlinear genetic algorithm

被引：4
|
作者：
Cui, ZH [1 ]
Zeng, JC [1 ]
Xu, YB [1 ]
机构：
[1] Taiyuan Heavy Machinery Inst, Div Syst Simulat & Comp Applicat, Taiyuan 030024, Shanxi, Peoples R China
来源：
2003 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-5, PROCEEDINGS | 2003年
关键词：
dynamic circle crossover operator; nonlinear genetic algorithm; nonlinear mutation operator;
D O I：
10.1109/ICMLC.2003.1259795
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Through mechanism analysis of simple genetic algorithm (SGA), 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, the nonlinear genetic algorithm is introduced, and different nonlinear genetic operators with some probability 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
收藏
页码：1836 / 1840
页数：5
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