Metaheuristic design of feedforward neural networks: A review of two decades of research
OPEN ACCESS
Loading...
Author / Producer
Date
2017-04
Publication Type
Journal Article
ETH Bibliography
no
Citations
Altmetric
OPEN ACCESS
Data
Abstract
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.
Permanent link
Publication status
published
Editor
Book title
Journal / series
Volume
60
Pages / Article No.
97 - 116
Publisher
Elsevier
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
MACHINE LEARNING (ARTIFICIAL INTELLIGENCE); NEURAL NETWORKS + CONNECTIONISM (ARTIFICIAL INTELLIGENCE)
Organisational unit
03276 - Schmitt, Gerhard (emeritus) / Schmitt, Gerhard (emeritus)