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Computational Intelligence and Neuroscience
Volume 2017, Article ID 4367342, 11 pages
Research Article

GA-Based Membrane Evolutionary Algorithm for Ensemble Clustering

School of Management Science and Engineering, Shandong Normal University, Jinan 250014, China

Correspondence should be addressed to Yanhua Wang; moc.361@72003145551, Xiyu Liu; nc.ude.unds@uilyx, and Laisheng Xiang; moc.361@6633slx

Received 21 June 2017; Revised 25 September 2017; Accepted 24 October 2017; Published 16 November 2017

Academic Editor: Leonardo Franco

Copyright © 2017 Yanhua Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Ensemble clustering can improve the generalization ability of a single clustering algorithm and generate a more robust clustering result by integrating multiple base clusterings, so it becomes the focus of current clustering research. Ensemble clustering aims at finding a consensus partition which agrees as much as possible with base clusterings. Genetic algorithm is a highly parallel, stochastic, and adaptive search algorithm developed from the natural selection and evolutionary mechanism of biology. In this paper, an improved genetic algorithm is designed by improving the coding of chromosome. A new membrane evolutionary algorithm is constructed by using genetic mechanisms as evolution rules and combines with the communication mechanism of cell-like P system. The proposed algorithm is used to optimize the base clusterings and find the optimal chromosome as the final ensemble clustering result. The global optimization ability of the genetic algorithm and the rapid convergence of the membrane system make membrane evolutionary algorithm perform better than several state-of-the-art techniques on six real-world UCI data sets.