  In recent years, Evolutionary Algorithms (EAs) have frequently been adopted
to evolve instances for optimization problems that pose difficulties for one
algorithm while being rather easy for a competitor and vice versa. Typically,
this is achieved by either minimizing or maximizing the performance difference
or ratio which serves as the fitness function. Repeating this process is useful
to gain insights into strengths/weaknesses of certain algorithms or to build a
set of instances with strong performance differences as a foundation for
automatic per-instance algorithm selection or configuration. We contribute to
this branch of research by proposing fitness-functions to evolve instances that
show large performance differences for more than just two algorithms
simultaneously. As a proof-of-principle, we evolve instances of the
multi-component Traveling Thief Problem~(TTP) for three incomplete TTP-solvers.
Our results point out that our strategies are promising, but unsurprisingly
their success strongly relies on the algorithms' performance complementarity.
