Chemo-inspired Genetic Algorithm for Optimizing the Piecewise Aggregate Approximation

Muhammad Marwan Muhammad Fuad

Abstract

In a previous work we presented DEWPAA: an improved version of the piecewise aggregate approximation representation method of time series. DEWPAA uses differential evolution to set weights to different segments of the time series according to their information content. In this paper we use a hybrid of bacterial foraging and genetic algorithm (CGA) to set the weights of the different segments in our improved piecewise aggregate approximation. Our experiments show that the new hybrid gives better results in time series classification.

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Paper Citation


in Harvard Style

Muhammad Fuad M. (2015). Chemo-inspired Genetic Algorithm for Optimizing the Piecewise Aggregate Approximation . In Proceedings of the International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-758-075-8, pages 205-210. DOI: 10.5220/0005277302050210


in Bibtex Style

@conference{icores15,
author={Muhammad Marwan Muhammad Fuad},
title={Chemo-inspired Genetic Algorithm for Optimizing the Piecewise Aggregate Approximation},
booktitle={Proceedings of the International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},
year={2015},
pages={205-210},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005277302050210},
isbn={978-989-758-075-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - Chemo-inspired Genetic Algorithm for Optimizing the Piecewise Aggregate Approximation
SN - 978-989-758-075-8
AU - Muhammad Fuad M.
PY - 2015
SP - 205
EP - 210
DO - 10.5220/0005277302050210