An efficient multi-objective evolutionary adaptive conjunction for high dimensional problems in linguistic fuzzy modelling

Adaptive connectors as conjunction operators of the inference system is one of the methodologies to improve the accuracy of fuzzy rule based systems by means of local adaptation of the inference process to each rule of the rule base. They are usually implemented through the classic adaptive t-norms, but when dealing with high-dimensional problems (several variables and/or instances) the adaptation of their parameters becomes problematic. In this paper, we propose a new adaptive conjunction connector and an associated multi-objective evolutionary learning algorithm which is more efficient and thus suitable for using adaptive connectors in high dimensional problems. The proposal is compared in an experimental study with the use of a well known efficient adaptive t-norm from the literature as conjunction operator. The results obtained on five regression problems confirm the effectiveness of the presented proposal in terms of efficiency, but also in terms of simplicity and compactness of the obtained models.