A derivative-free framework for optimizing a non-smooth range-based contrast function in order to estimate independent components is presented. The proposed algorithm employs the von-Mises Fisher (vMF) distribution to draw random samples in the cross-entropy (CE) method, thereby intrinsically maintaining the unit-norm constraint that removes the scaling indeterminacy in independent component analysis (ICA) problem. Empirical studies involving natural images show how this approach outperforms popular schemes [1] in terms of the separation performance.