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Recently, many different possibilities to extend the Linguistic Fuzzy Modeling have been considered in the specialized literature with the aim of introducing a trade-off between accuracy and interpretability. These approaches are not isolated and can be combined among them when they have complementary characteristics, such as the hierarchical linguistic rule learning and the weighted linguistic rule learning. In this paper, we propose the hybridization of both techniques to derive Hierarchical Systems of Weighted Linguistic Rules. To do so, an evolutionary optimization process jointly performing a rule selection and the rule weight derivation has been developed. The proposal has been tested with two real-world problems achieving good results.