ArticleComputational intelligence and neuroscience2017
GA-Based Membrane Evolutionary Algorithm for Ensemble Clustering.
Article in Computational intelligence and neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- A survey: to identify plant leaf diseases by feature extraction methods and classification techniques.Planta · 2025Review
- Neoscytalidium novaehollandiae causes dieback on Pinus eldarica and its potential for infection of urban forest trees.Scientific reports · 2022Article
- A Self-Adjusting Search Domain Method-Based Genetic Algorithm for Solving Flexible Job Shop Scheduling Problem.Computational intelligence and neuroscience · 2022Article
- The Mongolian Vowel Acoustic Model Based on the Clustering Algorithm.Computational intelligence and neuroscience · 2022Article
- Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery.Molecular diversity · 2021Article
- Characterization and synthetic biology of lipase from Bacillus amyloliquefaciens strain.Archives of microbiology · 2020Article
- A Complex Chained P System Based on Evolutionary Mechanism for Image Segmentation.Computational intelligence and neuroscience · 2020Article
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3 authors.
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Abstract
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.
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