  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 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, sometimes even better, than the best performance known for
static parameters. We complement our theoretical results with a rigorous
empirical study confirming what the asymptotic runtime results suggest.
