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A memetic imperialist competitive algorithm with chaotic maps for multi-layer neural network training

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journal contribution
posted on 18.03.2019, 13:37 by Seyed Jalaleddin Mousavirad, Azam Asilian Bidgoli, Hossein Ebrahimpour-Komleh, Gerald Schaefer
The performance of artificial neural networks (ANNs) is largely dependent on the success of the training process. Gradient descent-based methods are the most widely used training algorithms but have drawbacks such as ending up in local minima. One approach to overcome this is to use population-based algorithms such as the imperialist competitive algorithm (ICA) which is inspired by the imperialist competition between countries. In this paper, we present a new memetic approach for neural network training to improve the efficacy of ANNs. Our proposed approach – Memetic Imperialist Competitive Algorithm with Chaotic Maps (MICA-CM) – is based on a memetic ICA and chaotic maps, which are responsible for exploration of the search space, while back-propagation is used for an effective local search on the best solution obtained by ICA. Experiment results confirm our proposed algorithm to be highly competitive compared to other recently reported methods.

History

School

  • Science

Department

  • Computer Science

Published in

International Journal of Bio-Inspired Computation

Volume

14

Issue

4

Pages

227 - 236

Citation

MOUSAVIRAD, S.J. ... et al., 2019. A memetic imperialist competitive algorithm with chaotic maps for multi-layer neural network training. International Journal of Bio-Inspired Computation, 14 (4), pp.227-236.

Publisher

© Inderscience

Version

AM (Accepted Manuscript)

Publisher statement

This paper was accepted for publication in the journal International Journal of Bio-Inspired Computation and the definitive published version is available at https://doi.org/10.1504/IJBIC.2019.103961.

Acceptance date

08/12/2018

Publication date

2019-11-27

ISSN

1758-0366

Language

en

Exports