A generic adaptive simulation algorithm for component-based simulation systems

The state of a model may strongly vary during simulation, and with it also the simulation's computational demands. Adapting the simulation algorithm to these demands at runtime can therefore improve the overall performance. Although this is a general and cross-cutting concern, only few simulation systems offer re-usable 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' suitability during a simulation run. We evaluate the approach by executing models from two modeling formalisms used in computational biology.