Companies are increasingly conscious of the importance of Enterprise Architecture (EA) to represent and manage
IT and business in a holistic way. EA modelling has become decisive to achieve models that accurately represents
behaviour and assets of companies and lead them to make appropriate business decisions. Although EA representations can be manually modelled by experts, automatic EA modelling methods have been proposed to
deal with drawbacks of manual modelling, such as error-proneness, time-consumption, slow and poor readaptation, and cost. However, automatic modelling is not effective for the most abstract concepts in EA like
strategy or motivational aspects. Thus, companies are demanding hybrid approaches that combines automatic
with manual modelling. In this context there are no clear relationships between the input artefacts (and mining
techniques) and the target EA viewpoints to be automatically modelled, as well as relationships between the
experts' roles and the viewpoints to which they might contribute in manual modelling. Consequently, companies
cannot make informed decisions regarding expert assignments in EA modelling projects, nor can they choose
appropriate mining techniques and their respective input artefacts. This research proposes a decision support
system whose core is a genetic algorithm. The proposal first establishes (based on a previous literature review)
the mentioned missing relationships and EA model specifications. Such information is then employed using a
genetic algorithm to decide about automatic, manual or hybrid modelling by selecting the most appropriate
input artefacts, mining techniques and experts. The genetic algorithm has been optimized so that the system aids
EA architects to maximize the accurateness and completeness of EA models while cost (derived from expert
assignments and unnecessary automatic generations) are kept under control
