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

Generating Instances with Performance Differences for More Than Just Two Algorithms