Differential evolution algorithm-based kernel parameter selection for Fukunaga-Koontz Transform subspaces construction

2015 ◽  
Author(s):  
Hamidullah Binol ◽  
Abdullah Bal ◽  
Huseyin Cukur
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Parameter Selection ◽  
Kernel Parameter ◽  
Evolution Algorithm ◽  
Selection For

scholarly journals A Convergent Differential Evolution Algorithm with Hidden Adaptation Selection for Engineering Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Zhongbo Hu ◽  
Shengwu Xiong ◽  
Zhixiang Fang ◽  
Qinghua Su
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Population Diversity ◽  
Engineering Optimization ◽  
De Algorithm ◽  
Self Adaptation ◽  
Evolution Algorithm ◽  
Selection For ◽  
Adaptation Scheme

Many improved differential Evolution (DE) algorithms have emerged as a very competitive class of evolutionary computation more than a decade ago. However, few improved DE algorithms guarantee global convergence in theory. This paper developed a convergent DE algorithm in theory, which employs a self-adaptation scheme for the parameters and two operators, that is, uniform mutation and hidden adaptation selection (haS) operators. The parameter self-adaptation and uniform mutation operator enhance the diversity of populations and guarantee ergodicity. The haS can automatically remove some inferior individuals in the process of the enhancing population diversity. The haS controls the proposed algorithm to break the loop of current generation with a small probability. The breaking probability is a hidden adaptation and proportional to the changes of the number of inferior individuals. The proposed algorithm is tested on ten engineering optimization problems taken from IEEE CEC2011.

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