Metaheuristic design of feedforward neural networks: a review of two decades of research

Ojha, V. K. ORCID: https://orcid.org/0000-0002-9256-1192, Abraham, A. and Snasel, V. (2017) Metaheuristic design of feedforward neural networks: a review of two decades of research. Engineering Applications of Artificial Intelligence, 60. pp. 97-116. ISSN 0952‐1976 doi: 10.1016/j.engappai.2017.01.013

Abstract/Summary

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/82143
Item Type	Article
Refereed	Yes
Divisions	Interdisciplinary Research Centres (IDRCs) > Centre for the Mathematics of Planet Earth (CMPE) Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Publisher	Elsevier
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