  Despite the possibility to quickly compute reachable sets of large-scale
linear systems, current methods are not yet widely applied by practitioners.
The main reason for this is probably that current approaches are not
push-button-capable and still require to manually set crucial parameters, such
as time step sizes and the accuracy of the used set representation -- these
settings require expert knowledge. We present a generic framework to
automatically find near-optimal parameters for reachability analysis of linear
systems given a user-defined accuracy. To limit the computational overhead as
much as possible, our methods tune all relevant parameters during runtime. We
evaluate our approach on benchmarks from the ARCH competition as well as on
random examples. Our results show that our new framework verifies the selected
benchmarks faster than manually-tuned parameters and is an order of magnitude
faster compared to genetic algorithms.
