Research on Dynamic Clonal Selection Algorithm Combined with Artificial Fish School

2011 ◽  
Vol 63-64 ◽  
pp. 552-556
Author(s):  
Yu Ling Tian ◽  
Yu Hao Liu
Keyword(s):  
Production Efficiency ◽  
Simulation Experiment ◽  
Clonal Selection ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Rapid Convergence ◽  
Fish School ◽  
Clone Selection ◽  
Improved Algorithm

As in the dynamic clone selection algorithm, the detector use factor is low, the overall importance is bad, this article proposed that the behavior of follows, gathers which has the overall importance and the rapid convergence in the artificial school of fish algorithm applicant in the dynamic clone selection algorithm detector generation phase. Meanwhile, the efficiency of algorithm is improved, and many questions which stochastically the detector takes are solved. The simulation experiment indicated that the improved algorithm has the advantage of the artificial school fish algorithm and made up the question that earlier period to restrain slowly, the detector production efficiency low in its own system.

Feature Selection Based on Clonal Selection Algorithm

2011 ◽  
pp. 180-203
Author(s):  
Xiangrong Zhang ◽  
Fang Liu
Keyword(s):  
Feature Selection ◽  
Optimization Problem ◽  
Clonal Selection ◽  
Good Choice ◽  
Affinity Maturation ◽  
Clonal Selection Algorithm ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Rapid Convergence ◽  
Optimal Feature Subset

The problem of feature selection is fundamental in various tasks like classification, data mining, image processing, conceptual learning, and so on. Feature selection is usually used to achieve the same or better performance using fewer features. It can be considered as an optimization problem and aims to find an optimal feature subset from the available features according to a certain criterion function. Clonal selection algorithm is a good choice in solving an optimization problem. It introduces the mechanisms of affinity maturation, clone, and memorization. Rapid convergence and good global searching capability characterize the performance of the corresponding operations. In this study, the property of rapid convergence to global optimum of clonal selection algorithm is made use of to speed up the searching of the most appropriate feature subset among a huge number of possible feature combinations. Compared with the traditional genetic algorithm-based feature selection, the clonal selection algorithm-based feature selection can find a better feature subset for classification. Experimental results on datasets from UCI learning repository, 16 types of Brodatz textures classification, and synthetic aperture radar (SAR) images classification demonstrated the effectiveness and good performance of the method in applications.

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