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, Forthcoming.
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
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