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, by learning from examples.
@article{urn:eudml:doc:39111,
title = {Multi-stage genetic fuzzy systems based on the iterative rule learning approach.},
journal = {Mathware and Soft Computing},
volume = {4},
year = {1997},
pages = {233-249},
zbl = {0893.68121},
language = {en},
url = {http://dml.mathdoc.fr/item/urn:eudml:doc:39111}
}
González, Antonio; Herrera, Francisco. Multi-stage genetic fuzzy systems based on the iterative rule learning approach.. Mathware and Soft Computing, Tome 4 (1997) pp. 233-249. http://gdmltest.u-ga.fr/item/urn:eudml:doc:39111/