To design more effective algorithms for Independent Component Analysis (ICA), a general framework of Gradient Descent Algorithms (GDAs) for ICA was proposed, which covers many popular algorithms such as Infomax, Minimization of Mutual Information (MMI), Maximum Likelihood Estimation (MLE) and so on. This framework was derived from a new theory of the contrast functions for ICA based on the superadditive (or subadditive) function of class II. For better performances, the Equivariant Adaptive Separation via Independence (EASI) form was generalized and used as the updating rule. An example of using the framework was also shown based on the quadratic entropy. Furthermore, a fast method of computing the gradient in the example was proposed and the simulation proved its validity. The results demonstrate that this framework is a useful tool to discover more effective algorithms for ICA.