In Part II on generic heuristics we discussed the cornerstones of evolutionary economic programs with regards to their institutional frames and general semantics. In this Part III we moved on and elaborated on the trajectories from semantic to synthetic programming. Algorithms provide an ideal language structure for synthetic reasoning in a formalized way. Following Beinhocker (2011) we regard evolution as computation in general, as a unique set of search and sort algorithms. Evolutionary economic programs can be implemented with regard to the generic rule-based approach (Dopfer and Potts 2008) in a bottom-up way, a proposition of Part II developed further in Chapter 12. As many scholars have emphasized the rule-based approach with emphasis on the meso level as structure and process component offers a rich semantic as well as synthetic programming environment for formalized socioeconomic narratives. Institutions evolve in a modular way and shape recursively structure and process for social learning. Generic institutionalism refers to a bottom-up methodology to model elaborated heuristics. Models are not constructed for the purpose of prognosis or prediction but for the knowledge transfer of dynamic ideas and concepts insofar as we follow a pedagogic and didactic modelling strategy and believe that models entail dynamic visions of economic conceptions. These visions are encapsulated in generic evolutionary economic programs of institutional change and wait for simulation experiments. Computer experiments make generic institutionalism to an empirical discipline with focus on experience in a pragmatist logic.