We apply DOGMA, a GA-based theory revision system, to MDL-based rule enhancement in supervised concept learning. The system takes as input classification data and a rule-based classification theory, produced by some rule-based learner, and builds a second model of the data. The search for the new model is guided by a MDL-based complexity measure. The proposed methodology offers a partial solution both to the local minima trap of fast greedy learners, and to the time complexity problem of GA-based learners. As an example we show how the system improves rules produced by C4.5.