Three objective genetics-based machine learning for linguisitc rule extraction
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-objective rule mining using genetic algorithms
Information Sciences: an International Journal - Special issue: Soft computing data mining
Multi-Objective Machine Learning (Studies in Computational Intelligence) (Studies in Computational Intelligence)
International Journal of Approximate Reasoning
Parallel distributed genetic fuzzy rule selection
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Genetic Fuzzy Systems: Recent Developments and Future Directions; Guest editors: Jorge Casillas, Brian Carse
Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases
Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases
Rule-based modeling: precision and transparency
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
On generating FC3 fuzzy rule systems from data usingevolution strategies
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Selecting fuzzy if-then rules for classification problems using genetic algorithms
IEEE Transactions on Fuzzy Systems
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This paper briefly reviews genetic algorithm-based approaches to the design of fuzzy systems. In the 1990s, genetic algorithms were mainly used for the accuracy maximization of fuzzy systems. Various aspects of fuzzy systems were optimized by genetic algorithms such as the fuzzy partition of each input variable, the number of fuzzy rules, and the consequent part of each fuzzy rule. The accuracy maximization of fuzzy systems for training data, however, tends to increase their complexity. That is, the accuracy maximization often degrades the interpretability of fuzzy systems through the increase in their complexity. Some studies in the late 1990s tried to find a good tradeoff (i.e., compromise) between the accuracy and the complexity of fuzzy systems. The latest trend in the design of fuzzy systems is their evolutionary multiobjective design. A number of non-dominated fuzzy systems with different accuracy-complexity tradeoffs can be obtained by a single run of multiobjective approaches. In this paper, we briefly review the above-mentioned main stream of research on fuzzy system design.