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Genetic Algorithms (GA) is a method based on natural selection in the theory of biological evolution, which has been widely applied to solve numerous optimization problems in diverse fields. However, the canonical GA is more likely to get stuck at a local optimum and thereby leads to premature convergence. To overcome such inconvenience, a fuzzy adaptive GA (FAGA) is proposed based on fuzzy clustering...
Fuzzy K-prototypes is a very efficient algorithm for processing large scale mixed data set, but the selection of initial clustering center has an important impact on the clustering effect of algorithm. FKP algorithm is improved by using genetic algorithm in this paper. Seeking the initial clustering center for fuzzy K-prototypes algorithm by using genetic algorithm overcomes the shortcoming effectively,...
This paper applies the artificial fish swarm algorithm (AFSA) to fuzzy clustering. An improved AFSA with adaptive visual and adaptive step is proposed. AFSA enhances the performance of the fuzzy C-means (FCM) algorithm. A computational experiment shows that AFSA improved FCM out performs both the conventional FCM algorithm and the genetic algorithm (GA) improved FCM.
The parallel genetic algorithm (PGA) realizes converging rate greatly as a result of fully displaying the natural parallelism of GA. This paper is just about applying this idea to fuzzy c-mean clustering, introducing immunity density adjustment mechanism, and proposing a new FCM clustering method - IGA-FCM double groups parallel clustering algorithm (another form of PGA). In order to realize the algorithm,...
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