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Download fileA memetic imperialist competitive algorithm with chaotic maps for multi-layer neural network training
journal contribution
posted on 2019-03-18, 13:37 authored by Seyed Jalaleddin Mousavirad, Azam Asilian Bidgoli, Hossein Ebrahimpour-Komleh, Gerald SchaeferGerald SchaeferThe 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 ComputationVolume
14Issue
4Pages
227 - 236Citation
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
© InderscienceVersion
- 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
2018-12-08Publication date
2019-11-27ISSN
1758-0366Publisher version
Language
- en