Miguel A. De Vega

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This paper deals with the learning of the membership functions for Mamdani Fuzzy Systems – the number of labels of the variables and the tuning of them – in order to obtain a set of Linguistic Fuzzy Systems with different trade-offs between accuracy and complexity, through the use of a two-level evolutionary multi-objective algorithm. The presented(More)
This work aims to develop a practical study of the uni-norms as rule antecedent aggregation operator family in Linguistic Fuzzy Modeling. Although they are well known from a theoretical point of view, they have only recently been introduced in a few specific and recent applications. Uninorms are parameterized operators that combine the membership values in(More)
This paper presents a proposal to improve REGAL, a concept learning system based on a distributed genetic algorithm that learns first-order logic multi-modal concept descriptions in the field of classification tasks. This algorithm has been a pioneer system and source of inspiration for others. Studying the philosophy and experimental behaviour of REGAL, we(More)
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