IEEE Computational Intelligence Magazine - August 2021 - 2

CIM Editorial Board
Editor-in-Chief
Chuan-Kang Ting
National Tsing Hua University
Department of Power Mechanical Engineering
No. 101, Section 2, Kuang-Fu Road
Hsinchu 30013, TAIWAN
(Phone) +886-3-5742611
(Email) ckting@pme.nthu.edu.tw
Chuan-Kang Ting
National Tsing Hua University, TAIWAN
Editor's
Editor's
Remarks
Founding Editor-in-Chief
Gary G. Yen, Oklahoma State University, USA
Past Editors-in-Chief
Kay Chen Tan, Hong Kong Polytechnic
University, HONG KONG
Hisao Ishibuchi, Southern University of Science and
Technology, CHINA
Editors-At-Large
Piero P. Bonissone, Piero P Bonissone Analytics
LLC, USA
David B. Fogel, Natural Selection, Inc., USA
Vincenzo Piuri, University of Milan, ITALY
Marios M. Polycarpou, University of Cyprus,
CYPRUS
Jacek M. Zurada, University of Louisville, USA
Associate Editors
José M. Alonso, University of Santiago de
Compostela, SPAIN
Erik Cambria, Nanyang Technological University,
SINGAPORE
Liang Feng, Chongqing University, CHINA
Barbara Hammer, Bielefeld University, GERMANY
Eyke Hüllermeier, University of Munich, GERMANY
Sheng Li, University of Georgia, USA
Hsuan-Tien Lin, National Taiwan University,
TAIWAN
Hongfu Liu, Brandeis University, USA
Zhen Ni, Florida Atlantic University, USA
Yusuke Nojima, Osaka Prefecture University, JAPAN
Nelishia Pillay, University of Pretoria, SOUTH AFRICA
Danil Prokhorov, Toyota R&D, USA
Kai Qin, Swinburne University of Technology,
AUSTRALIA
Rong Qu, University of Nottingham, UK
Ming Shao, University of Massachusetts
Dartmouth, USA
Vincent S. Tseng, National Chiao Tung University,
TAIWAN
Kyriakos G. Vamvoudakis, Georgia Tech, USA
Nishchal K. Verma, Indian Institute of Technology
Kanpur, INDIA
Handing Wang, Xidian University, CHINA
Dongrui Wu, Huazhong University of Science and
Technology, CHINA
Bing Xue, Victoria University of Wellington,
NEW ZEALAND
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Digital Object Identifier 10.1109/MCI.2021.3056260
Digital Object Identifier 10.1109/MCI.2021.3084387
Date of current version: 15 July 2021
2 IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | AUGUST 2021
T
he focus on evolutionary neural architecture search in this
issue has driven me to ponder the evolution of biological
neural networks, or rather, the human brain. Researchers in
neuroanatomy found that the cognitive and mental development
of humans is attributed to the increase of our brain size throughout
evolution. Yet bigger is not always better. It was also discovered
that, once the brain reaches a certain size, further growth
will only render the brain less efficient. In addition, the brain is limited and affected
by its inherent architecture and signal processing time. These discoveries in neuroscience
are interestingly analogous to the advances in neural networks and
evolutionary computation. Stacking of perceptrons empowers artificial neural networks
to solve complex problems but detracts from their efficiency. Indeed, the
human brain evolution and the artificial neural network evolution both face the
trade-off between capacity and efficiency. The workings of nature truly give us a
lot to mull over.
This issue includes five Features articles. The first article proposes an evolutionary
multi-objective model compression approach to simultaneously optimize the model
size and accuracy of a deep neural network. The second and third articles adopt the
notion of self-supervised learning in the neural architecture evolution, resulting in
state-of-the-art performance. To improve the accuracy of wind speed forecasting, the
fourth article uses an evolutionary algorithm to optimize the architecture of dendritic
neural regression. The fifth article presents a self-adaptive mutation strategy for blockbased
evolutionary neural architecture search on convolutional neural networks.
In the Columns, the article proposes a multi-view feature construction method
based on genetic programming and ensemble techniques. The approach can automatically
generate high-level features from multiple views; moreover, a new fitness function
is developed to improve the discriminability of these features. Experimental
results show the effectiveness of the proposed approach in rolling bearing fault diagnosis
with a limited amount of data.
We hope you will enjoy the articles in this issue. If you have any suggestions or
feedback for this magazine, please do not hesitate to contact me at ckting@pme.nthu
.edu.tw.
The Evolution of Neural Networks

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IEEE Computational Intelligence Magazine - August 2021

Table of Contents for the Digital Edition of IEEE Computational Intelligence Magazine - August 2021