Multi-stage genetic fuzzy systems based on the iterative rule learning approach.

Antonio González; Francisco Herrera

Mathware and Soft Computing (1997)

  • Volume: 4, Issue: 3, page 233-249
  • ISSN: 1134-5632

Abstract

top
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.

How to cite

top

González, Antonio, and Herrera, Francisco. "Multi-stage genetic fuzzy systems based on the iterative rule learning approach.." Mathware and Soft Computing 4.3 (1997): 233-249. <http://eudml.org/doc/39111>.

@article{González1997,
abstract = {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.},
author = {González, Antonio, Herrera, Francisco},
journal = {Mathware and Soft Computing},
keywords = {Algoritmos genéticos; Lógica difusa; Inteligencia artificial; Controladores difusos; Teoría del aprendizaje; Métodos iterativos; Etapas; Sistemas expertos; genetic algorithms; adaptive search techniques; genetic fuzzy systems},
language = {eng},
number = {3},
pages = {233-249},
title = {Multi-stage genetic fuzzy systems based on the iterative rule learning approach.},
url = {http://eudml.org/doc/39111},
volume = {4},
year = {1997},
}

TY - JOUR
AU - González, Antonio
AU - Herrera, Francisco
TI - Multi-stage genetic fuzzy systems based on the iterative rule learning approach.
JO - Mathware and Soft Computing
PY - 1997
VL - 4
IS - 3
SP - 233
EP - 249
AB - 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.
LA - eng
KW - Algoritmos genéticos; Lógica difusa; Inteligencia artificial; Controladores difusos; Teoría del aprendizaje; Métodos iterativos; Etapas; Sistemas expertos; genetic algorithms; adaptive search techniques; genetic fuzzy systems
UR - http://eudml.org/doc/39111
ER -

NotesEmbed ?

top

You must be logged in to post comments.