DocumentCode :
724046
Title :
Rough set knowledge reduction algorithm based on chaos genetic algorithm
Author :
Pan Wei ; Zhu Wenliang ; Liu Sili
Author_Institution :
Electr. Detection Dept., Shenyang Artillery Acad., Shenyang, China
fYear :
2015
fDate :
23-25 May 2015
Firstpage :
1382
Lastpage :
1387
Abstract :
In order to obtain a valid property smallest relative reduction, this article proposed a sort of rough set knowledge reduction algorithm based on chaos genetic algorithm. The algorithm loads the chaotic variable in population genetic algorithm, making minor disturbances to progeny groups with chaos variables and adjusting perturbation amplitude gradually in the searching process to make the new algorithms not only enhancing the local search capability but also maintain the characteristics of the global optimization algorithm. At last verified by one classic example, it achieved good results whether in accuracy of reduction or in average run algebra.
Keywords :
chaos; genetic algorithms; perturbation techniques; rough set theory; average run algebra; chaos genetic algorithm; global optimization algorithm; local search capability; perturbation amplitude; rough set knowledge reduction algorithm; Algebra; Chaos; Genetic algorithms; Genetics; Optimization; Sociology; Statistics; chaos genetic algorithm; crossover probability; knowledge reduction; mutation probability; rough set;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Decision Conference (CCDC), 2015 27th Chinese
Conference_Location :
Qingdao
Print_ISBN :
978-1-4799-7016-2
Type :
conf
DOI :
10.1109/CCDC.2015.7162134
Filename :
7162134
Link To Document :
بازگشت