Numerical Coding and Unfair Average Crossover in GA for Fuzzy Rule Extraction in Dynamic Environments

@inproceedings{Nomura1995NumericalCA,
  title={Numerical Coding and Unfair Average Crossover in GA for Fuzzy Rule Extraction in Dynamic Environments},
  author={Tatsuya Nomura and Tsutomu Miyoshi},
  booktitle={IEEE/Nagoya-University World Wisepersons Workshop},
  year={1995}
}
1 I n t r o d u c t i o n Though fuzzy inference rules have usually been constructed through trial and error by humans, many methods with machine learning such as neural networks and genetic algorithms have recently been proposed for automatic rule extraction from a given set of input-output data examples. In particular, a number of fuzzy rule extraction methods with genetic algorithms (GA) have recently been proposed [i][2][7]. Most of them aim at selecting the most appropriate rules for… CONTINUE READING

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