Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Accuracy Improvements in Linguistic Fuzzy Modeling
Accuracy Improvements in Linguistic Fuzzy Modeling
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
International Journal of Approximate Reasoning
A Pareto-based multi-objective evolutionary approach to the identification of Mamdani fuzzy systems
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A 2-tuple fuzzy linguistic representation model for computing with words
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
A Three-Objective Evolutionary Approach to Generate Mamdani Fuzzy Rule-Based Systems
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
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We propose a multi-objective evolutionary algorithm to generate a set of fuzzy rule-based systems with different trade-offs between accuracy and complexity. The novelty of our approach resides in performing concurrently learning of rules and learning of the membership functions which define the meanings of the labels used in the rules. To this aim, we represent membership functions by the linguistic 2-tuple scheme, which allows the symbolic translation of a label by considering only one parameter, and adopt an appropriate two-variable chromosome coding. Results achieved by using a modified version of PAES on a real problem confirm the effectiveness of our approach in increasing the accuracy and decreasing the complexity of the solutions in the approximated Pareto front with respect to the single objective-based approach.