Learning Fuzzy Classifiers with Evolutionary Algorithms

This paper illustrates an evolutionary algorithm, which learns classifiers, represented as sets of fuzzy rules, from a data set containing past experimental observations of a phenomenon. The approach is applied to a benchmark dataset made available by the machine learning community.

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