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| Content Provider | ACM Digital Library |
|---|---|
| Author | Helms, Tobias Rybacki, Stefan Ewald, Roland Uhrmacher, Adelinde M. |
| Copyright Year | 2015 |
| Abstract | The state and structure of a model may vary during a simulation and, thus, also its computational demands. Adapting simulation algorithms to these demands at runtime can therefore improve their performance. While this is a general and cross-cutting concern, only few simulation systems offer reusable support for this kind of runtime adaptation. We present a flexible and generic mechanism for the runtime adaptation of component-based simulation algorithms. It encapsulates simulation algorithms applicable to a given problem and employs reinforcement learning to explore the algorithms’ performance during a simulation run. We evaluate our approach on a modeling formalism from computational biology and on a benchmark model defined in PDEVS, thereby investigating a broad range of options for improving its learning capabilities. |
| Starting Page | 1 |
| Ending Page | 24 |
| Page Count | 24 |
| File Format | |
| ISSN | 10493301 |
| e-ISSN | 15581195 |
| DOI | 10.1145/2821509 |
| Volume Number | 26 |
| Issue Number | 1 |
| Journal | ACM Transactions on Modeling and Computer Simulation (TOMACS) |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2015-10-19 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Adaptive algorithms Component-based simulation software Reinforcement learning |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Science Applications Modeling and Simulation |
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