the effects of solution density in the search space on finding spatially robust solutions
The common definition for robust solutions considers a solution robust if it remains optimal (or near optimal) when the parameters defining the fitness function are perturbed. We call this parameter robustness or temporal robustness. In this paper we propose an alternate definition for robustness, which we call spatial or solution robustness, if both the solution and the neighbourhood around the solution has high fitness. With this definition, we created a set of functions with useful properties to allow for the testing of solution robustness. We then focus on the effect of the precision (density) of the search space and find that it has a drastic effect on both the number of solutions and their quality.
representation genetic algorithms robust optimization