  Heuristic optimisation algorithms are in high demand due to the overwhelming
amount of complex optimisation problems that need to be solved. The complexity
of these problems is well beyond the boundaries of applicability of exact
optimisation algorithms and therefore require modern heuristics to find
feasible solutions quickly. These heuristics and their effects are almost
always evaluated and explained by particular problem instances. In previous
works, it has been shown that many such algorithms show structural bias, by
either being attracted to a certain region of the search space or by
consistently avoiding regions of the search space, on a special test function
designed to ensure uniform 'exploration' of the domain. In this paper, we
analyse the emergence of such structural bias for Differential Evolution (DE)
configurations and, specifically, the effect of different mutation, crossover
and correction strategies. We also analyse the emergence of the structural bias
during the run-time of each algorithm. We conclude with recommendations of
which configurations should be avoided in order to run DE unbiased.
