Metamodel-based dynamic algorithm configuration using artificial neural networks

We consider the problem of configuring algorithms dynamically by selecting algorithm parameter values adaptively. The research is motivated by the time dependency of system parameters throughout algorithm runtime in servicing systems: Depending on the customer arrival rate, switching algorithm param...

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Veröffentlicht in:	International journal of general systems Jg. 53; H. 1; S. 41 - 71
Hauptverfasser:	Dunke, Fabian, Nickel, Stefan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Abingdon Taylor & Francis 02.01.2024 Taylor & Francis LLC
Schlagworte:	Algorithm configuration Algorithms artificial neural network Artificial neural networks Configurations Customer services Customers dynamic decision making Metamodels Parameters Performance prediction simulation metamodeling Switching theory
ISSN:	0308-1079, 1563-5104
Online-Zugang:	Volltext
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