Recent advances in machine learning have been able to make improvements over the state-of-the-art regarding semantic similarity measurement techniques. In fact, we have all seen how classical techniques have given way to promising neural techniques. Nonetheless, these new techniques have a weak point: they are hardly interpretable. For this reason, we have oriented our research towards the design of strategies being able to be accurate enough but without sacrificing their interpretability. As a result, we have obtained a strategy for the automatic design of semantic similarity controllers based on fuzzy logics, which are automatically identified using genetic algorithms (GAs). After an exhaustive evaluation using a number of well-known benchmark datasets, we can conclude that our strategy fulfills both expectations: it is able of achieving reasonably good results, and at the same time, it can offer high degrees of interpretability.