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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Parallel Distributed Implementation of Genetics-Based Machine Learning for Fuzzy Classifier Design
verfasst von : Yusuke Nojima, Shingo Mihara, Hisao Ishibuchi
Erschienen in: Simulated Evolution and Learning
Verlag: Springer Berlin Heidelberg
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Evolutionary algorithms have been successfully applied to design fuzzy rule-based classifiers. They are used for attribute selection, fuzzy set selection, rule selection, membership function tuning, and so on. Genetics-based machine learning (GBML) is one of the promising evolutionary algorithms for classifier design. It can find an appropriate combination of antecedent sets for each rule in a classifier. Although GBML has high search ability, it needs long computation time especially for large data sets. In this paper, we apply a parallel distributed implementation to our fuzzy genetics-based machine learning. In our method, we divide not only a population but also a training data set into subgroups. These subgroups are assigned to CPU cores. Through computational experiments on large data sets, we show the effectiveness of the proposed parallel distributed implementation.