During the last two decades, evolutionary computation has been utilized successfully to create fuzzy systems known as Evolutionary Fuzzy Systems (EFSs) or Genetic Fuzzy Systems (GFSs). This is because the use of evolutionary computation can improve numerous capabilities of fuzzy systems, such as generalization for unknown and uncertain data sets, interpretability for users, and applicability to real-world issues. Evolutionary optimization methods encompass a wide range of algorithms that are frequently employed to solve complex optimization issues that traditional optimization algorithms cannot solve efficiently. Fuzzy system optimization is a complicated optimization task that involves both continuous, integer, and combinatorial problems. For example, the selection of fuzzy system input attributes, the design of the fuzzy system structure, the selection of membership functions, and the selection of inference operators are all combinatorial optimization problems, whereas the selection of membership function parameters and fuzzy rules is a continuous optimization problem. Furthermore, when we consider both the interpretability and accuracy of fuzzy systems, optimization of a fuzzy system becomes a many-objective optimization problem. As a result, applications of many-objective optimization approaches and their hardware implementation are critical.

This Special Issue will collate research focusing on breakthroughs in fuzzy-based evolutionary algorithms that use many-objective optimization to maintain the variety and convergence of the distribution of the solution set over the Pareto front (PF). We welcome both original research and review articles.

Potential topics include but are not limited to the following:

• Genetic algorithms and programming in fuzzy systems
• Differential evolution algorithms in fuzzy systems
• Nature-inspired optimization methods in deep neuro-fuzzy systems
• Many-objective optimization of fuzzy systems using nature-inspired optimization methods
• Swarm Intelligence algorithms in fuzzy systems
• Many-objective optimization for estimating knee, nadir points, and constraint handling methods
• Many-objective optimization with objectives' constraints
• Many-objective optimization algorithms' robustness improvement
• Many-objective optimization methods in type-2 fuzzy systems
• Hybrid approach to many-objective optimization methods in fuzzy systems
• Many-objective optimization methods in interpretable fuzzy systems
• Many-objective optimization for engineering design problems