IEEE Computational Intelligence Magazine - May 2023 - 31

Evolutionary MultiObjective
Optimization in
Searching for Various
Antimicrobial Peptides
Yiping Liu, Xinyi Zhang,
and Yuansheng Liu
Hunan University, CHINA
Yansen Su
Anhui University, CHINA
Xiangxiang Zeng
Hunan University, CHINA
Gary G. Yen
Oklahoma State University, USA
Abstract-Antimicrobial peptides (AMPs), which are parts ofthe
innate immune response found among all classes of life, are
promising in broad-spectrum antibiotics and drug-resistant
infection treatments. Although AMPs effectively kill bacteria,
numerous AMPs widely distributed in the sequence space remain
unknown to humans. Therefore, the de novo design of AMPs
involves the exploration ofvast sequence space to identify peptides
with high antimicrobial activity and good diversity among the
known AMPs. Computational intelligence approaches have
successfully identified some AMPs; however, most ofthem fail to
address the diversity ofthe obtained AMPs. This paper reports an
evolutionary multi-objective approach for AMP design to
optimize both the antimicrobial activity and diversity among
identified AMPs.Our approach employs a deep learning model to
Digital Object Identifier 10.1109/MCI.2023.3245731
Date ofcurrent version: 6April 2023
1556-603X ß 2023 IEEE
Corresponding author: Yansen Su (e-mail: suyansen1985@163.com)
MAY 2023 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 31
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IEEE Computational Intelligence Magazine - May 2023

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