An improved simulated annealing genetic algorithm of EEG feature selection in sleep stage

2016 ◽  
Author(s):  
Yundong Ji ◽  
Xiangeng Bu ◽  
Jinwei Sun ◽  
Zhiyong Liu
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Simulated Annealing ◽  
Sleep Stage ◽  
Simulated Annealing Genetic Algorithm

scholarly journals A New Feature Selection Method for Hyperspectral Image Classification Based on Simulated Annealing Genetic Algorithm and Choquet Fuzzy Integral

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Hongmin Gao ◽  
Lizhong Xu ◽  
Chenming Li ◽  
Aiye Shi ◽  
Fengchen Huang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Hyperspectral Image ◽  
Selection Method ◽  
Hyperspectral Images ◽  
Fuzzy Integral ◽  
High Dimensions ◽  
Simulated Annealing Genetic Algorithm

Hyperspectral remote sensing technology is a rapidly developing new integrated technology that is widely used in numerous areas. Rich spectral information from hyperspectral images can aid in the classification and recognition of the ground objects. However, the high dimensions of hyperspectral images cause redundancy in information. Hence, the high dimensions of hyperspectral data must be reduced. This paper proposes a hybrid feature selection strategy based on the simulated annealing genetic algorithm (SAGA) and the Choquet fuzzy integral (CFI). The band selection method is proposed from subspace decomposition, which combines the simulated annealing algorithm with the genetic algorithm in choosing different cross-over and mutation probabilities, as well as mutation individuals. Then, the selecting bands are further refined by CFI. Experimental results show that the proposed method can achieve higher classification accuracy than traditional methods.

Computational Intelligence in Cross Docking

2014 ◽  
Vol 2 (1) ◽  
pp. 1-8
Author(s):  
Bo Xing
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Simulated Annealing ◽  
Tabu Search ◽  
Computational Intelligence ◽  
Simple Concept ◽  
Simulated Annealing Genetic Algorithm ◽  
Cross Docking ◽  
Open Questions ◽  
Key Issues

Cross docking is a practice in logistics with the main operations of goods flow directly from receiving to the shipping docks without stopping or being put away into storage. It is a simple concept to talk about, but a challenging one to implement. So far, many different approaches have been followed in order to improve the efficiency of a cross docking system. However, as the complexity increases, the use of computational intelligence (CI) in those problems is becoming a unique tool of imperative value. In this paper, different CI methods, such as Tabu search, simulated annealing, genetic algorithm, and fuzzy logic. The key issues in implementing the proposed approaches are discussed, and finally the open questions are highlighted.

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