The need for trading off interpretability and accuracy is intrinsic to the use of fuzzy systems.
The obtaining of accurate but also human-comprehensible fuzzy systems played a key role …

This paper provides an in-depth review of the optimal design of type-1 and type-2 fuzzy
inference systems (FIS) using five well known computational frameworks: genetic-fuzzy …

Evolutionary fuzzy systems are one of the greatest advances within the area of
computational intelligence. They consist of evolutionary algorithms applied to the design of …

Fuzzy Systems have shown their utility for solving a wide range of problems in different
application domains. The use of Genetic Algorithms for designing Fuzzy Systems allows us …

Abstract Evolutionary Fuzzy Systems are a successful hybridization between fuzzy systems
and Evolutionary Algorithms. They integrate both the management of imprecision …

The use of genetic algorithms for designing fuzzy systems provides them with the learning
and adaptation capabilities and is called genetic fuzzy systems (GFSs). This topic has …

The main aim of this paper is to present MOGUL, a Methodology to Obtain Genetic fuzzy rule‐
based systems Under the iterative rule Learning approach. MOGUL will consist of some …

Although fuzzy systems demonstrated their ability to solve different kinds of problems in
various applications, there is an increasing interest on augmenting them with learning …

One of the problems associated to linguistic fuzzy modeling is its lack of accuracy when
modeling some complex systems. To overcome this problem, many different possibilities of …

The paper presents a multi-objective genetic approach to design interpretability-oriented
fuzzy rule-based classifiers from data. The proposed approach allows us to obtain systems …