Simulation replication is a necessity for all stochastic simulations. Its efficient execution is particularly important when additional techniques are used on top, such as optimization or sensitivity analysis. One way to improve replication efficiency is to ensure that the best configuration of the simulation system is used for execution. A selection of the best configuration is possible when the number of required replications is sufficiently high, even without any prior knowledge on simulator performance or problem instance. We present an adaptive replication mechanism that combines portfolio theory with reinforcement learning: it adapts itself to the given problem instance at runtime and can be restricted to an efficient algorithm portfolio.
