Multi-stage Genetic Fuzzy Systems Based on the Iterative Rule Learning Approach Multi-stage Genetic Fuzzy Systems Based on the Iterative Rule Learning Approach

@inproceedings{Alez1997MultistageGF,
  title={Multi-stage Genetic Fuzzy Systems Based on the Iterative Rule Learning Approach Multi-stage Genetic Fuzzy Systems Based on the Iterative Rule Learning Approach},
  author={A Gonzz Alez and Francisco Herrera and Antonio Gonz{\'a}lez},
  year={1997}
}
Genetic algorithms (GAs) represent a class of adaptive search techniques inspired by natural evolution mechanisms. The search properties of GAs make them suitable to be used in machine learning processes and for developing fuzzy systems, the so-called genetic fuzzy systems (GFSs). In this contribution, we discuss genetics-based machine learning processes presenting the iterative rule learning approach, and a special kind of GFS, a multi-stage GFS based on the iterative rule learning approach… CONTINUE READING
Highly Influential
This paper has highly influenced a number of papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 114 citations. REVIEW CITATIONS

Topics

Statistics

051015'98'00'02'04'06'08'10'12'14'16'18
Citations per Year

114 Citations

Semantic Scholar estimates that this publication has 114 citations based on the available data.

See our FAQ for additional information.