Clustering of moving vectors for evolutionary computation

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

3 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
CountryJapan
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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