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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 8/2019

07-06-2019 | Editorial

Special issue on evolutionary multi-objective optimization (EMO): theory and applications

Authors: Zhihua Cui, Xiao-Zhi Gao

Published in: International Journal of Machine Learning and Cybernetics | Issue 8/2019

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Excerpt

Evolutionary Multi-objective Optimization (EMO) is a collection of the state-of-the-art theoretical research, design challenges, and applications in the field of multi-objective optimization paradigms using evolutionary algorithms. It is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously. The EMO has been applied in many fields of science, including engineering, economics, and logistics, where the optimal decisions need to be made in the presence of the trade-offs between two or even more conflicting objectives. We believe that the series of works in this special issue provide a useful reference for learning the current progresses on the EMO. In total, six papers have been selected to reflect the call of the thematic vision. The contents of these studies are briefly described as follows. …
next article Evolutionary dynamic multi-objective optimization algorithm based on Borda count method

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Metadata
Title
Special issue on evolutionary multi-objective optimization (EMO): theory and applications
Authors
Zhihua Cui
Xiao-Zhi Gao
Publication date
07-06-2019
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 8/2019
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-019-00971-0

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