An algorithmic scheme for the solution of convex feasibility problems is proposed inwhich the end-points of strings of sequential projections onto the constraints are averaged. The scheme, employing Bregman projections, is analyzed with the aid of an extended product space formalism. For the case of orthogonal projections we give also a relaxed version. Along with the well-known purely sequential and fully simultaneous cases, the new scheme includes many other inherently parallel algorithmic options depending on the choice of strings. Convergence in the consistent case is proven and an application to optimization over linear inequalities is given.