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Distributed populations in Genetic Algorithms can make the search more smart, in that local minima may be skipped. However, when the global population is divided into small sub-populations, the ability of these sub-populations to evolve is set back because of their relatively small sizes. In this paper, a new method to manage the distributed populations in(More)
We present a new novel method of automatically generating a multi-variable fuzzy inference system from given sample sets. We rst decompose the sample set, say , into a cluster of sample sets associated with the given input variables, then compute the associated fuzzy rules and membership functions for each variable, independent of the other variables, by(More)
In this paper, we propose a hierarchical fuzzy system for high-dimensional data. We introduce a locally weighted scheme to the extraction of Takagi-Sugeno type rules. We apply the sequential least-squares method to estimate the linear model. A hierarchical clustering takes place in the product space of systems inputs and outputs, and each path from the root(More)
Genetic algorithms have proved to be a suit choice for fuzzy modeling, however, with the number of dimensions increase, genetic algorithm tends to be inefficient. In this paper, we present a combination of cooperative coevolutionary genetic algorithm (CCGA) with fuzzy system. The flexible coding scheme permits one chromosome correspond a rule. For a high(More)
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