.png){kind=icon}
.png){kind=icon}
Return
Clustering data using a modified integer genetic algorithm (IGA)
.png){kind=icon}
PRE
AI
DOI：10.1016/S0003-2670(97)00462-5.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper developed a modified genetic algorithm with integer representation (IGA) for cluster analysis problem. The IGA method expands the basic concepts of conventional GAs to include fitness scaling, a modified selection operator, and three newly proposed genetic operators, i.e., competition, self-reproduction and diversification. Moreover, a new clustering criterion was introduced and compared with the commonly used square-error criterion. Clustering of simulated and real chemical data showed that IGA consistently outperformed conventional GAs both in search efficiency and in search precision, and the introduced criterion provided better performance than the square-error criterion. (C) 1997 Elsevier Science B.V.
Keywords:
cluster analysis
integer genetic algorithm (IGA)
clustering criteria
genetic algorithm (GA)
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6
Papers:
3.3W
Citations:
6.1W
Organization
No organization information available