Differential evolution versus genetic algorithms: Towards symbolic aggregate approximation of non-normalized time series

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

3 Citations (Scopus)

Abstract

The differential evolution (DE) is a very powerful search method for solving many optimization problems. In this paper we present a new scheme (DESAX) based on the differential evolution to localize the breakpoints utilized with the symbolic aggregate approximation method; one of the most important symbolic representation techniques for times series data. We compare the new scheme with a previous one (GASAX), which is based on the genetic algorithms, and we show how the new scheme outperforms the original one. We also show how (DESAX) can be used for the symbolic aggregate approximation of non-normalized time series.

Original languageEnglish
Title of host publicationProceedings of International Database Engineering and Applications Symposium
PublisherACM
Pages205-210
Number of pages6
ISBN (Print)9781450312349
DOIs
Publication statusPublished - 28 Sep 2012
Externally publishedYes
Event16th International Database Engineering and Applications Symposium - Prague, Czech Republic
Duration: 8 Aug 201210 Aug 2012
Conference number: 16th

Publication series

NameACM International Conference Proceeding Series

Conference

Conference16th International Database Engineering and Applications Symposium
Abbreviated titleIDEAS 2012
CountryCzech Republic
CityPrague
Period8/08/1210/08/12

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Keywords

  • Differential evolution
  • Genetic algorithms
  • Symbolic aggregate approximation
  • Time series

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

Cite this

Fuad, M. M. M. (2012). Differential evolution versus genetic algorithms: Towards symbolic aggregate approximation of non-normalized time series. In Proceedings of International Database Engineering and Applications Symposium (pp. 205-210). (ACM International Conference Proceeding Series). ACM. https://doi.org/10.1145/2351476.2351501