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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Bibliographic Details

Published in:	International journal of general systems Vol. 53; no. 1; pp. 41 - 71
Main Authors:	Dunke, Fabian, Nickel, Stefan
Format:	Journal Article
Language:	English
Published:	Abingdon Taylor & Francis 02.01.2024 Taylor & Francis LLC
Subjects:	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 Access:	Get full text
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