  Generating instances of different properties is key to algorithm selection
methods that differentiate between the performance of different solvers for a
given combinatorial optimization problem. A wide range of methods using
evolutionary computation techniques has been introduced in recent years. With
this paper, we contribute to this area of research by providing a new approach
based on quality diversity (QD) that is able to explore the whole feature
space. QD algorithms allow to create solutions of high quality within a given
feature space by splitting it up into boxes and improving solution quality
within each box. We use our QD approach for the generation of TSP instances to
visualize and analyze the variety of instances differentiating various TSP
solvers and compare it to instances generated by a $(\mu+1)$-EA for TSP
instance generation.
