In a previous work we presented a new distance that we called the sigma gram distance, which is used to compute the similarity between two sequences. This distance is based on parameters which we computed through an optimization process that used the artificial bee colony; a bio-inspired optimization algorithm. In this paper we show how a hybrid of two optimization algorithms; bacterial foraging and differential evolution, when used to compute the parameters of the sigma gram distance, can yield better results than those obtained by applying artificial bee colony. This superiority in performance is validated through experiments on the same data sets to which artificial bee colony, on the same optimization problem, was tested.
|Number of pages||10|
|Journal||Procedia Computer Science|
|Publication status||Published - 1 Jan 2014|
|Event||International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2014 - Gdynia, Poland|
Duration: 15 Sep 2014 → 17 Sep 2014
Bibliographical note© 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
- Bacterial foraging
- Differential evolution
- Sigma gram distance
ASJC Scopus subject areas
- Computer Science(all)
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- School of Computing, Mathematics and Data Sciences - Assistant Professor Academic
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