IEEE Computational Intelligence Magazine - November 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.2021.3056264
Digital Object Identifier 10.1109/MCI.2021.3108297
Date of current version: 13 October 2021
2 IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | NOVEMBER 2021
L
everaging knowledge has been a key topic in the research of
computational intelligence, where the knowledge can be
acquired by humans or machines from the learning process
or search experience. Transferring the knowledge learnt, as
humans usually do, possesses a great potential for dealing with
new tasks in learning and optimization. In particular, evolutionary
multitasking renders an intriguing way to optimize multiple tasks
simultaneously through evolutionary mechanisms and knowledge transfer. Although
humans also multitask-at least I thought I could-our performance in multitasking
may neither come up to our expectations nor be comparable with machines.
Researchers in neuroscience and psychology have found that the human brain is not
built to perform tasks concurrently. Having multiple task goals actually causes interference
between our brain networks. Switching back and forth between tasks, i.e.,
context switching, increases the demand for more processing in our brains and eventually
leads to performance deterioration. Interestingly, the research also indicates that
our multitasking ability may be affected by evolution and the training of media multitasking
in modern life.
This issue features three articles in " Knowledge Transfer in Evolutionary Optimization. "
The first article introduces a generalized resource allocation framework for evolutionary
multitask optimization, especially under restrictive computational resources. To
address the issue of negative knowledge transfer among low-correlation tasks, the second
article considers inter-task gene similarity and mirror transform and integrates them into
the multifactorial evolutionary algorithm. The third article presents advanced designs for
memetic automation; notably, meme selection is devised to improve knowledge transfer
across the population.
In the Columns, the first article proposes a new data hiding scheme that enables transmitting
data to multiple receivers through a single neural network. The second article
applies evolutionary algorithms to design nanoparticle based drug delivery systems for
cancer treatment. In the Society Briefs, we congratulate the 2021 CIS Award recipients on
their remarkable accomplishments.
Finally, I would like to thank our CIS President, Bernadette Bouchon-Meunier, for
her leadership these past two years. The CIS has been thriving even under the hardships
the pandemic has brought. In particular, during her tenure as the President, she strove
to promote diversity in all activities within the society, and the CIS saw a rise especially
in the number of women in our committees. The CIS chapters are continuously growing
under her guidance and support. Once again, I heartily thank the President and
wish her the very best in her future endeavors.
Knowledge Transfer and Multitasking

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IEEE Computational Intelligence Magazine - November 2021

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