Parallel distributed implementation of genetics-based machine learning for fuzzy classifier design

Abstract

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. © 2010 Springer-Verlag.