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 re-adaptation, 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.

公司越来越意识到企业架构（EA）对于以整体方式表示和管理IT和业务的重要性。EA建模对于实现可准确表示公司行为和资产并引导他们做出适当业务决策的模型具有决定性作用。尽管EA表示可以由专家手动建模，但已经提出了自动EA建模方法，以解决手动建模的缺点，如易错，耗时，重新适应缓慢和较差以及成本高。但是，自动建模对于EA中最抽象的概念（如策略或动机方面）无效。因此，公司要求将自动建模与手动建模相结合的混合方法。在这种情况下，输入文物（和挖掘技术）与要自动建模的目标EA观点之间没有明确的关系，并且专家角色和他们可能在手动建模中做出贡献的观点之间也没有明确的关系。因此，公司无法就EA建模项目中的专家分配做出明智的决定，也无法选择适当的挖掘技术及其各自的输入人工制品。这项研究提出了一个决策支持系统，其核心是遗传算法。该提案首先（基于先前的文献综述）建立了所提到的缺失关系和EA模型规范。然后，使用遗传算法将这些信息用于决定自动，通过选择最合适的输入工件，挖掘技术和专家来进行手动或混合建模。遗传算法已经过优化，因此该系统可帮助EA架构师最大限度地提高EA模型的准确性和完整性，同时控制成本（由专家分配和不必要的自动生成）。