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 …

If complexity is your problem, learning classifier systems (LCSs) may offer a solution. These
rule‐based, multifaceted, machine learning algorithms originated and have evolved in the …

The potential and effectiveness of the newly developed Pareto-based multiobjective
evolutionary algorithms (MOEA) for solving a real-world power system multiobjective …

Coronary artery disease (CAD) is one of the major causes of mortality worldwide.
Knowledge about risk factors that increase the probability of developing CAD can help to …

This paper considers the automatic design of fuzzy-rule-based classification systems from
labeled data. The performance of classifiers and the interpretability of generated rules are of …

Abstract A Genetic Fuzzy System (GFS) is basically a fuzzy system augmented by a learning
process based on a genetic algorithm (GA). Fuzzy systems have demonstrated their ability to …

A non-dominated multiobjective evolutionary algorithm for extracting fuzzy rules in subgroup
discovery (NMEEF-SD) is described and analyzed in this paper. This algorithm, which is …

In this paper we propose GP-COACH, a Genetic Programming-based method for the
learning of COmpact and ACcurate fuzzy rule-based classification systems for High …

An anti-lock brake system based on fuzzy logic has been developed and optimized to cope
with changes in adherence road conditions. Conventional control systems have to be tuned …

In this paper, we propose a novel design of evolving fuzzy classifiers (EFCs) to handle
online multiclass classification problems in a data-streaming context. Therefore, we exploit …