NDLI: An effective hybrid crossover operator for genetic algorithms to solve k-means clustering problem

Content Provider	IEEE Xplore Digital Library
Author	Jianxin Wang Haiyan Zhang Xiaoli Dong Ben Xu Benjin Mei
Copyright Year	2010
Description	Author affiliation: School of Information, Beijing Forestry University, China (Jianxin Wang; Haiyan Zhang; Xiaoli Dong; Ben Xu; Benjin Mei)
Abstract	The k-means clustering problem is a famous problem with a variety of applications. It can be summarized as finding the best k representative centers for an input data set. K-means algorithm and its variations are known to be fast approximation iterative algorithms to the problem. However, several studies have shown that the genetic algorithm (GA) performs more favorably. In this paper, a new crossover operator for clustering GA is proposed. It combines string-coded crossover operator and real-coded crossover operator. Results from a series of experiments on benchmark data are quite encouraging, including that the newly proposed crossover operator performs better than both string-coded crossover operator and two versions of real-coded crossover operators. The way of coefficient selection for the combination is presented. In addition, the coding scheme and other genetic operations, such as selection and mutation, are discussed in detail.
Starting Page	2271
Ending Page	2275
File Size	215967
Page Count	5
File Format	PDF
ISBN	9781424459582
e-ISBN	9781424459612
DOI	10.1109/ICNC.2010.5584457
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering methods hybrid crossover operator Clustering algorithms Genetics Encoding Decoding genetic algorithms k-means clustering problem Biological cells Next generation networking
Content Type	Text
Resource Type	Article

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