Techniques of evolutionary algorithms (EA) rely heavily on the use of random number generators (RNGs). From initial population generation, through the specific canonical operators applied to create new temporary population, the use of randomness is pervasive through EAs.
Best quality is believed to stem from RNGs that produce number sequences that are uniform and uncorrelated.
Two different RNGs were applied to an EA, which calculated values for populations over time. The goal was to find out how quality of RNGs affect EAs performance.
How can the sensitiveness of the EA to the two RNGs be detected?