Differential Evolution (DE), a global optimization algorithm based on the concepts of Darwinian evolution, is popular for its simplicity and effectiveness in solving numerous real-world optimization problems in real-valued spaces. The effectiveness of DE is due to the differential mutation operator that allows DE to automatically adjust between the exploration/exploitation in its search moves. However, the performance of DE is dependent on the setting of control parameters such as the mutation factor and the crossover probability. Therefore, to obtain optimal performance preliminary tuning of the numerical parameters, which is quite timing consuming, is needed. Recently, different parameter adaptation techniques, which can automatically update the control parameters to appropriate values to suit the characteristics of optimization problems, have been proposed. However, most of the adaptation techniques try to adapt each of the parameter individually but do not take into account interaction between the parameters that are being adapted. In this paper, we introduce a DE self-adaptive scheme that takes into account the parameters dependencies by means of a multivariate probabilistic technique based on Gaussian Adaptation working on the parameter space. The performance of the DE algorithm with the proposed parameter adaptation scheme is evaluated on the benchmark problems designed for CEC 2014.