2C2-3 並列分散型ファジィ遺伝的機械学習の探索性能に対する個体移住操作の影響(最適化・進化計算)

概要

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Genetics-based machine learning (GBML) is one of the promising evolutionary algorithms for classifier design. It can construct rule-based classifiers from numerical data sets. Although GBML has high search ability, it needs long computation time especially for large data sets. We have already proposed parallel distributed fuzzy genetics-based machine learning to reduce the computational time. In our method, we divide not only a population but also a training data set into sub-populations and training data subsets, respectively. In parallel distributed approaches such as island models, a migration operator has been frequently used to avoid the premature convergence and increase the diversity of the sub-populations. In this paper, we apply a migration operator to our parallel distributed fuzzy GBML, and examine the effects on the search ability of our method.
一般社団法人日本機械学会の論文
2011-09-01