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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| Published in | International journal of general systems Vol. 53; no. 1; pp. 41 - 71 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Abingdon
Taylor & Francis
02.01.2024
Taylor & Francis LLC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0308-1079 1563-5104 |
| DOI | 10.1080/03081079.2023.2245124 |
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| Abstract | 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 parameters may be advisable to maintain quality of service. To this end, we develop a metamodel-based methodology for dynamic algorithm configuration: We first record algorithm performance under static system parameters. This knowledge is then translated into an artificial neural network (ANN) predicting performance for given system and algorithm parameters. The ANN finally serves as a metamodel determining optimal algorithm parameters dynamically when there is system parameter variation. Overall, the developed generic methodology for dynamic algorithm control facilitates a structured model-based approach to suitably respond to changing system conditions. The outline is adept to practical instantiation as demonstrated in two service systems where control parameters are adjusted adaptively to customer arrival rates. |
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| AbstractList | 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 parameters may be advisable to maintain quality of service. To this end, we develop a metamodel-based methodology for dynamic algorithm configuration: We first record algorithm performance under static system parameters. This knowledge is then translated into an artificial neural network (ANN) predicting performance for given system and algorithm parameters. The ANN finally serves as a metamodel determining optimal algorithm parameters dynamically when there is system parameter variation. Overall, the developed generic methodology for dynamic algorithm control facilitates a structured model-based approach to suitably respond to changing system conditions. The outline is adept to practical instantiation as demonstrated in two service systems where control parameters are adjusted adaptively to customer arrival rates. |
| Author | Dunke, Fabian Nickel, Stefan |
| Author_xml | – sequence: 1 givenname: Fabian surname: Dunke fullname: Dunke, Fabian email: fabian.dunke@kit.edu organization: Karlsruhe Institute of Technology – sequence: 2 givenname: Stefan surname: Nickel fullname: Nickel, Stefan organization: Karlsruhe Institute of Technology |
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| References | e_1_3_2_28_1 e_1_3_2_49_1 e_1_3_2_20_1 e_1_3_2_41_1 e_1_3_2_66_1 e_1_3_2_22_1 e_1_3_2_43_1 e_1_3_2_64_1 e_1_3_2_24_1 e_1_3_2_45_1 e_1_3_2_26_1 e_1_3_2_47_1 e_1_3_2_68_1 Battiti Roberto (e_1_3_2_10_1) 2008 e_1_3_2_60_1 e_1_3_2_16_1 e_1_3_2_39_1 e_1_3_2_9_1 e_1_3_2_18_1 e_1_3_2_7_1 e_1_3_2_31_1 e_1_3_2_54_1 e_1_3_2_33_1 e_1_3_2_52_1 e_1_3_2_12_1 e_1_3_2_35_1 e_1_3_2_58_1 e_1_3_2_5_1 e_1_3_2_37_1 e_1_3_2_56_1 e_1_3_2_3_1 Borodin Alan (e_1_3_2_14_1) 2005 e_1_3_2_50_1 e_1_3_2_71_1 e_1_3_2_27_1 e_1_3_2_29_1 e_1_3_2_42_1 Raúl Rojas. (e_1_3_2_59_1) 2013 e_1_3_2_65_1 e_1_3_2_21_1 e_1_3_2_44_1 e_1_3_2_63_1 e_1_3_2_23_1 e_1_3_2_46_1 e_1_3_2_69_1 e_1_3_2_25_1 e_1_3_2_48_1 e_1_3_2_67_1 Schwarz Hannes (e_1_3_2_62_1) 2019 e_1_3_2_61_1 e_1_3_2_40_1 e_1_3_2_17_1 e_1_3_2_38_1 e_1_3_2_8_1 e_1_3_2_19_1 e_1_3_2_2_1 e_1_3_2_30_1 e_1_3_2_55_1 e_1_3_2_11_1 e_1_3_2_32_1 e_1_3_2_53_1 e_1_3_2_6_1 e_1_3_2_13_1 e_1_3_2_34_1 e_1_3_2_4_1 e_1_3_2_15_1 e_1_3_2_36_1 e_1_3_2_57_1 e_1_3_2_51_1 e_1_3_2_70_1 |
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| SubjectTerms | Algorithm configuration Algorithms artificial neural network Artificial neural networks Configurations Customer services Customers dynamic decision making Metamodels Parameters Performance prediction simulation metamodeling Switching theory |
| Title | Metamodel-based dynamic algorithm configuration using artificial neural networks |
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