Clustering of moving vectors for evolutionary computation

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    4 Citations (Scopus)

    Abstract

    We propose a method for clustering moving vectors oriented around two different local optima and some methods for improving the clustering performance. Evolutionary computation is an optimization method for finding the global optimum iteratively using multiple individuals; we propose a method for estimating the global optimum mathematically using the moving vectors between parent individuals and their offspring. Our proposed clustering method is the first to tackle the extension of the estimation method to multi-modal optimization. We describe the algorithm of the clustering method, the improvements made to the method, and the estimation performance for two local optima.

    Original languageEnglish
    Title of host publicationProceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015
    EditorsMario Koppen, Azah Kamilah Muda, Kun Ma, Bing Xue, Hideyuki Takagi, Ajith Abraham
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages169-174
    Number of pages6
    ISBN (Electronic)9781467393607
    DOIs
    Publication statusPublished - Jun 15 2016
    Event7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015 - Fukuoka, Japan
    Duration: Nov 13 2015Nov 15 2015

    Publication series

    NameProceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015

    Other

    Other7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015
    Country/TerritoryJapan
    CityFukuoka
    Period11/13/1511/15/15

    All Science Journal Classification (ASJC) codes

    • Computer Vision and Pattern Recognition
    • Signal Processing
    • Control and Optimization
    • Modelling and Simulation

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