Learning to Learn Evolutionary Algorithm: A Learnable Differential Evolution

Research on evolutionary optimization has flourished for several decades. Now it has come to a turning point. With the advancement of artificial intelligence, especially deep learning and reinforcement learning, it is becoming appealing to rethink the design and development of evolutionary algorithm...

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Veröffentlicht in:	IEEE transactions on emerging topics in computational intelligence Jg. 7; H. 6; S. 1 - 16
Hauptverfasser:	Liu, Xin, Sun, Jianyong, Zhang, Qingfu, Wang, Zhenkun, Xu, Zongben
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.12.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Artificial intelligence Deep learning deep reinforcement learning differential evolution Evolutionary algorithms Evolutionary computation Genetic algorithms Learning to learn evolutionary algorithm Machine learning Markov processes mutation strategy Neural networks Optimization parameter control Search problems Sociology Statistics Trajectory Trajectory optimization Urban areas
ISSN:	2471-285X, 2471-285X
Online-Zugang:	Volltext
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