  Most evolutionary algorithms have multiple parameters and their values
drastically affect the performance. Due to the often complicated interplay of
the parameters, setting these values right for a particular problem (parameter
tuning) is a challenging task. This task becomes even more complicated when the
optimal parameter values change significantly during the run of the algorithm
since then a dynamic parameter choice (parameter control) is necessary.
  In this work, we propose a lazy but effective solution, namely choosing all
parameter values (where this makes sense) in each iteration randomly from a
suitably scaled power-law distribution. To demonstrate the effectiveness of
this approach, we perform runtime analyses of the $(1+(\lambda,\lambda))$
genetic algorithm with all three parameters chosen in this manner. We show that
this algorithm on the one hand can imitate simple hill-climbers like the
$(1+1)$ EA, giving the same asymptotic runtime on problems like OneMax,
LeadingOnes, or Minimum Spanning Tree. On the other hand, this algorithm is
also very efficient on jump functions, where the best static parameters are
very different from those necessary to optimize simple problems. We prove a
performance guarantee that is comparable to the best performance known for
static parameters. For the most interesting case that the jump size $k$ is
constant, we prove that our performance is asymptotically better than what can
be obtained with any static parameter choice. We complement our theoretical
results with a rigorous empirical study confirming what the asymptotic runtime
results suggest.
