Title :
An adaptive genetic algorithm based on rough set attribute reduction
Author :
Liu, BingXiang ; Liu, Feng ; Cheng, Xiang
Author_Institution :
Sch. of Inf. Eng., JDZ Ceramic Inst., Jingdezhen, China
Abstract :
Attribute reduction is one of important problem of rough set theory. In order to get effectively attribute reduction, we presented an algorithm of attribute reduction of rough set based on improved adaptive genetic algorithm (IAGA). IAGA adjusts the crossover probability and mutation probability of each individual according to individual fitness value. The optimization capability and the convergence velocity of adaptive GA are improved.
Keywords :
genetic algorithms; probability; rough set theory; GA; adaptive genetic algorithm; attribute reduction; crossover probability; mutation probability; rough set theory; Biological cells; Convergence; Encoding; Gallium; Genetic algorithms; Information systems; Set theory; Adaptive; Attribute reduction; Genetic Algorithm; Rough set;
Conference_Titel :
Biomedical Engineering and Informatics (BMEI), 2010 3rd International Conference on
Conference_Location :
Yantai
Print_ISBN :
978-1-4244-6495-1
DOI :
10.1109/BMEI.2010.5639635
