Multi-stage genetic fuzzy systems based on the iterative rule learning approach
Antonio González, Francisco Herrera Triguero · 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 socalled 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. Keywords: Fuzzy logic, fuzzy rules, genetic algorithms, machine learning. 1 Introduction Genetic Algorithms (GAs) are search algorithms that use operations found in natural genetics to guide the trek through a search space. GAs are theoretically and empirically proven to provide robust search capabilities in complex spaces, offering a valid approach to problems requiring efficient and effective searching. Much of the interest in GAs is due to the fact that they provide a...