  When designing a benchmark problem set, it is important to create a set of
benchmark problems that are a good generalization of the set of all possible
problems. One possible way of easing this difficult task is by using
artificially generated problems. In this paper, one such single-objective
continuous problem generation approach is analyzed and compared with the COCO
benchmark problem set, a well know problem set for benchmarking numerical
optimization algorithms. Using Exploratory Landscape Analysis and Singular
Value Decomposition, we show that such representations allow us to further
explore the relations between the problems by applying visualization and
correlation analysis techniques, with the goal of decreasing the bias in
benchmark problem assessment.
