Three objective genetics-based machine learning for linguisitc rule extraction
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Multiobjective Optimization: Theoretical Advances and Applications (Advanced Information and Knowledge Processing)
Multi-Objective Machine Learning (Studies in Computational Intelligence) (Studies in Computational Intelligence)
International Journal of Approximate Reasoning
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases
Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Selecting fuzzy if-then rules for classification problems using genetic algorithms
IEEE Transactions on Fuzzy Systems
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Evolutionary multiobjective optimization (EMO) is one of the most active research areas in evolutionary computation. EMO algorithms have been successfully used in various application areas. Among them are multiobjective design of neural networks and fuzzy systems. Especially, fuzzy system design has often been discussed as multiobjective problems. This is because we have two conflicting objectives in the design of fuzzy systems: accuracy maximization and complexity minimization. In this paper, we first explain some basic concepts in multiobjective optimization, a basic framework of EMO algorithms and some hot research issues in the EMO community. Next we explain EMO-based approaches to the design of fuzzy systems. We demonstrate through computational experiments that a large number of non-dominated fuzzy systems with different accuracy-complexity tradeoffs can be obtained by a single run of an EMO algorithm. Then we describe the use of EMO algorithms in other areas such as neural networks, genetic programming, clustering, feature selection, and data mining.