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• W1532864123 abstract "In this paper, we examine two genetic-algorithm based approaches to the design of fuzzy-rule-based systems for multi-dimensional pattern classification problems. One approach handles a set of fuzzy if-then rules as an individual in genetic algorithms. A fitness value is assigned to each rule set, and a crossover operator is applied to a pair of rule sets. The other approach is a fuzzy classifier system where a single fuzzy if-then rule is handled as an individual. A fitness value is assigned to each fuzzy if-then rule, and a crossover operator is applied to a pair of rules. The main aim of this paper is to examine the ability of these two approaches to design a fuzzy-rule-based system with high classification performance. This examination is done by computer simulations on a real-life pattern classification problem. Moreover the classification performance of fuzzy-rule-based systems is compared with that of non-fuzzy classification methods." @default.
• W1532864123 created "2016-06-24" @default.
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• W1532864123 date "2002-12-24" @default.
• W1532864123 modified "2023-09-27" @default.
• W1532864123 title "Genetic-algorithm-based approaches to the design of fuzzy systems for multi-dimensional pattern classification problems" @default.
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• W1532864123 doi "https://doi.org/10.1109/icec.1996.542366" @default.
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