  We present two adaptive schemes for dynamically choosing the number of
parallel instances in parallel evolutionary algorithms. This includes the
choice of the offspring population size in a (1+$\lambda$) EA as a special
case. Our schemes are parameterless and they work in a black-box setting where
no knowledge on the problem is available. Both schemes double the number of
instances in case a generation ends without finding an improvement. In a
successful generation, the first scheme resets the system to one instance,
while the second scheme halves the number of instances. Both schemes provide
near-optimal speed-ups in terms of the parallel time. We give upper bounds for
the asymptotic sequential time (i.e., the total number of function evaluations)
that are not larger than upper bounds for a corresponding non-parallel
algorithm derived by the fitness-level method.
