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
相关论文
共 50 条
  • [31] Two new mutation operators for enhanced search and optimization in evolutionary programming
    Chellapilla, K
    Foge, D
    APPLICATIONS OF SOFT COMPUTING, 1997, 3165 : 260 - 269
  • [32] A mixed mutation strategy evolutionary programming combined with species conservation technique
    Dong, HB
    He, J
    Huang, HK
    Hou, W
    MICAI 2005: ADVANCES IN ARTIFICIAL INTELLIGENCE, 2005, 3789 : 593 - 602
  • [33] A new mutation operator based on the T probability distribution in evolutionary programming
    Gong, Wenyin
    Cai, Zhihua
    Lu, Xinwei
    Jiang, Siwei
    PROCEEDINGS OF THE FIFTH IEEE INTERNATIONAL CONFERENCE ON COGNITIVE INFORMATICS, VOLS 1 AND 2, 2006, : 675 - 679
  • [34] Mutation Strategy Based on Step Size and Survival Rate for Evolutionary Programming
    Hong, Libin
    Liu, Chenjian
    Cui, Jiadong
    Liu, Fuchang
    DISCRETE DYNAMICS IN NATURE AND SOCIETY, 2021, 2021
  • [35] A Mixed Mutation Approach for Evolutionary Programming Based on Guided Selection Strategy
    Anik, Md. Tanvir Alam
    Ahmed, Saif
    2013 INTERNATIONAL CONFERENCE ON INFORMATICS, ELECTRONICS & VISION (ICIEV), 2013,
  • [36] A new mutation rule for evolutionary programming motivated from backpropagation learning
    Choi, DH
    Oh, SY
    IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2000, 4 (02) : 188 - 190
  • [37] Novel bi-subgroup evolutionary programming based on chaotic mutation
    Zhang, M
    Wang, XJ
    Ji, D
    DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SERIES B-APPLICATIONS & ALGORITHMS, 2005, 1 : 125 - 129
  • [38] Nonlinear Time Series Prediction Using LS-SVM with Chaotic Mutation Evolutionary Programming for Parameter Optimization
    XU Rui-Rui CHEN Tian-Lun GAO Cheng-Feng Department of Physics
    Communications in Theoretical Physics, 2006, 45 (04) : 641 - 646
  • [39] Nonlinear time series prediction using LS-SVM with chaotic mutation evolutionary programming for parameter optimization
    Xu, RR
    Chen, TL
    Gao, CF
    COMMUNICATIONS IN THEORETICAL PHYSICS, 2006, 45 (04) : 641 - 646
  • [40] Times Series Discretization Using Evolutionary Programming
    Rechy-Ramirez, Fernando
    Acosta Mesa, Hector-Gabriel
    Mezura-Montes, Efren
    Cruz-Ramirez, Nicandro
    ADVANCES IN SOFT COMPUTING, PT II, 2011, 7095 : 225 - +
12345