Building energy consumption used for internal heating and cooling purposes is one of the most viral research topics. Retrofitting and renovation activities in building applications aim towards utilizing modern construction materials, with improved thermal and insulation characteristics. It is more than evident that such an approach leads to an improved thermal shield for the building (improving passive building elements). In addition well calibrated rule based control designs are also being adopted in the last decades as a way to improve the energy efficiency in buildings (improving active elements management). Both of the above approaches though are considered as time static since disturbances with high uncertainty (weather conditions, human presence and activity) along with the unavoidable construction material aging phenomena affect building behavior and HVAC dynamics. As a result control recalibration activities seem more than necessary to maintain energy efficiency. Followed by the rapid evolution in the computing machines sector and simulation software kits, research effort has been focused on model assisted and co-simulation based control strategies which utilize the available computational power of modern machines towards improving energy efficiency and comfort levels through appropriate design Building Optimization and Control (BOC) systems, utilizing available system models. However the main drawback in model-assisted strategies is the fact that they heavily rely on the available building model which requires a tedious offline pre-application period including many simulation tests and/or field experiments so as to fine tune and tailor manually the model and consequently the control logic implemented. Moreover, no matter how elaborate the building model is, aging characteristics and uncertain disturbances are factors which call for re-designing (periodically) the available simulation model and the respective control strategies. This paper considers an alternative approach to BOC system design. The main attribute of the proposed methodology is that it can provide automated fine-tuning of the BOC system: no human intervention or a simulation model are required for the initial deployment of the controller as well as for the continuously applied fine-tuning procedure. Real-life experiments performed in a highly energy demanding building in Tel Aviv Israel, during spring time, demonstrate that the proposed approach can effectively provide intelligent decisions that none of the currently employed rule/event-based strategy can replicate. (C) 2016 Published by Elsevier B.V.