In multiobjective decision proplems many values must be assigned, such as the importance of the different criteria and the values of the alternatives with respect to subjective criteria. Since these assignments are approximate, it is very important to analyze the sensitivity of results when small modifications of the assignments are made. When solving a multicriteria decision problem, it is desirable to choose a decision function that leads to a solution as stable as possible. Proposed here is a method based on genetic programming that produces better decision functions than the commonly used ones. The theoretical expectations are validated by case studies.