Learning Adaptive Differential Evolution Algorithm From Optimization Experiences by Policy Gradient

Differential evolution is one of the most prestigious population-based stochastic optimization algorithm for black-box problems. The performance of a differential evolution algorithm depends highly on its mutation and crossover strategy and associated control parameters. However, the determination p...

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Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 25; H. 4; S. 666 - 680
Hauptverfasser:	Sun, Jianyong, Liu, Xin, Back, Thomas, Xu, Zongben
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.08.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptive algorithms Adaptive control Adaptive differential evolution Algorithms Convergence Deep learning Evolutionary algorithms Evolutionary computation global optimization Machine learning Markov processes Mutation Optimization policy gradient (PG) Process control Process parameters Reinforcement learning reinforcement learning (RL) Sociology Statistics
ISSN:	1089-778X, 1941-0026
Online-Zugang:	Volltext
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