Evolutionary programming with only using exponential mutation

被引：4
|
作者：
Narihisa, H. [1 ]
Kohmoto, K. [1 ]
Taniguchi, T.
Ohta, M.
Katayama, K.
机构：
[1] Klinki Univ, Sch Biol Oriented Sci & Technol, Dept Intelligence Syst, Wakayama 6496493, Japan
来源：
2006 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-6 | 2006年
关键词：
D O I：
10.1109/CEC.2006.1688358
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The individual of population in standard self-adaptive evolutionary programming (EP) is composed as a pair of objective variable and strategy parameter. Therefore, EP must evolve both objective variable and strategy parameter. In standard evolutionary programming (CEP), these evolutions are implemented by mutation based on only Gaussian random number. On the other hand, fast evolutionary programming (FEP) uses Cauchy random number as evolution of objective variable and exponential evolutionary programming (EEP) uses exponential random number as evolution of objective variable. However, all of these EP (CEP, FEP and EEP) commonly uses Gaussian random number as evolution of strategy parameter. In this paper, we propose new EEP algorithm (NEP) which uses double exponential random number for both evolution of objective variable and strategy parameter. The experimental results show that this new algorithm (NEP) outperforms the existing CEP and FEP.
引用
收藏
页码：552 / +
页数：3
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