Title :
Genetic Algorithm Based on Evolution Strategy and the Application in Data Mining
Author :
Zhu, Xiaoyuan ; Yu, Yongquan ; Guo, Xueyan
Author_Institution :
Dept. of Comput., Guangdong Baiyun Univ., Guangzhou
Abstract :
When traditional genetic algorithm is applied in mining association rules, it would get into local prematurity and becomes slow-footed in convergence.The paper brings forward that evolution strategy´s excellence is applied in genetic algorithmpsilas evolutional process. Then optimized genetic algorithm is used for mining association rules. In order to test the validity of the arithmetic, this paper presents a example of data mining about finance service. Research result indicates that the arithmetic can enhance search speed and data accuracy. Consequently it can effectively drive farther development of data mining.
Keywords :
data mining; genetic algorithms; association rules; data mining; evolution strategy; genetic algorithm; Algorithm design and analysis; Arithmetic; Artificial intelligence; Association rules; Computer science education; Convergence; Data analysis; Data mining; Forward contracts; Genetic algorithms; Data Mining; Evolution Strategy; Genetic Agorithm;
Conference_Titel :
Education Technology and Computer Science, 2009. ETCS '09. First International Workshop on
Conference_Location :
Wuhan, Hubei
Print_ISBN :
978-1-4244-3581-4
DOI :
10.1109/ETCS.2009.192