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 …

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 …

This paper presents the tuning of the structure and parameters of a neural network using an
improved genetic algorithm (GA). It is also shown that the improved GA performs better than …

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

In this paper, a TSK-type recurrent fuzzy network (TRFN) structure is proposed. The proposal
calls for the design of TRFN by either neural network or genetic algorithms depending on the …

The search for interpretable reinforcement learning policies is of high academic and
industrial interest. Especially for industrial systems, domain experts are more likely to deploy …

This study presents an evolutionary neural fuzzy network, designed using the functional-link-
based neural fuzzy network (FLNFN) and a new evolutionary learning algorithm. This new …

Forecasting tourism demand is a crucial issue in the tourism industry and is generally seen
to be one of the most complex functions of tourism management. With the accurate …

In this article, a novel parallel tracking control optimization algorithm is first proposed for
partially unknown fuzzy interconnected systems. In the existing standard optimal tracking …

In recent years, reinforcement learning (RL) techniques have achieved great success in
many different applications. However, their heavy reliance on complex deep neural …