A central question when parallelizing evolutionary algorithms is the choice
of the number of parallel instances. In practice optimal parameter settings are
often hard to find due to limited information about the optimization problem
under consideration. We present two adaptive schemes for dynamically choosing
the number of instances in each generation. These schemes work in a black-box
setting where no knowledge on the function at hand is available.