A genetic-based method for training fuzzy systems
Refereed conference paper presented and published in conference proceedings


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AbstractIn this paper, a genetic-based method for training fuzzy classification systems is proposed. The genetic algorithm, called genetic algorithm with no genetic operators (GANGO), neither needs to use the conventional genetic operators nor to store the population throughout the evolution process, but still has the same search mechanisms as conventional genetic algorithms. The novelty of the proposed training approach lies in (a) the new scheme of encoding a fuzzy system based on the interpretation of the values of the components of a fuzzy relationship matrix as the sample probabilities of genes; this, together with no requirement on storing the population, contributes to a dramatic decrease in storage requirement and computational cost; (b) the automatic elimination of irrelevant fuzzy rules using a fitness reassignment strategy at the gene level and a weight truncation strategy. The proposed training method is successfully applied to train a fuzzy system for the classification of real-world remote sensing data.
All Author(s) ListLeung Y., Gao Y., Zhang W.
Name of Conference10th IEEE International Conference on Fuzzy Systems
Start Date of Conference02/12/2001
End Date of Conference05/12/2001
Place of ConferenceMelbourne
Country/Region of ConferenceAustralia
Year2001
Month12
Day1
Volume Number1
Pages123 - 126
LanguagesEnglish-United Kingdom
KeywordsFuzzy rule, Fuzzy system, Genetic algorithm

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