DocumentCode :
1564305
Title :
An improved genetic approach
Author :
Fuyan, Liu ; Chouyong, Chen ; Shaoyi, Lv
Author_Institution :
Dept. of Inf. Manage., Hangzhou Dianzi Univ.
Volume :
2
fYear :
2005
Firstpage :
641
Lastpage :
644
Abstract :
In this paper, we propose an improved genetic algorithm, which is based on an incremental genetic K-means algorithm. This approach combines an incremental genetic algorithm with K-means clustering. The main difference of our approach from the original lies in that we get rid of illegal solutions, which were allowed in the original, during whole evolution process of the genetic algorithm from initialization to its termination. The improvement in our approach is accomplished through changing the way of generating initial population in initialization phase and changing the method of dealing with empty clusters in mutation operation. Thus, the illegal solutions were eliminated from our algorithm and resulting more efficient time performance. Experimental results show that our improved genetic approach is promising
Keywords :
genetic algorithms; pattern clustering; K-means clustering; incremental genetic K-means algorithm; mutation operation; Algorithm design and analysis; Biological cells; Clustering algorithms; Genetic algorithms; Genetic mutations; Information management; Robustness;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks and Brain, 2005. ICNN&B '05. International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-9422-4
Type :
conf
DOI :
10.1109/ICNNB.2005.1614714
Filename :
1614714
Link To Document :
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