Differential Evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through the similar computational steps as employed by a standard Evolutionary Algorithm (EA). However, unlike the traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used, which makes the scheme self-organizing in this respect. Scale Factor is a very important control parameter of DE. This article describes a very competitive yet very simple form of adaptation technique for tuning the scale factor, on the run, without any user intervention. The adaptation strategy is based on the objective function value of individuals in DE population. Comparison with the most competitive and expensive variants of DE over the well-known numerical benchmarks reflects the superiority of this simple parameter automation strategy in terms of accuracy, convergence speed, and robustness.