Method for the optimization of learning systems parameters
نویسندگان
چکیده
From a very broad point of view a Fuzzy System (FS) is any Fuzzy Logic – Based System, where Fuzzy Logic can be used either as the basis for the representation of various forms of knowledge systems, or to model the interactions and relationships among the system variables. Genetic Algorithms (GAs) are general purpose research algorithms which use principles inspired by natural genetics to evolve solutions to problems. One of the main problems related to GAs is to find the values of the parameters that the GAs use (population size, probabilities of applying genetic operators, etc.). Nowadays, there is no general theory which would describe parameters of GA when applied to any problem. Some recommendations are often offered after some empiric studies of GAs. On the other hand, there are some tendencies to integrate Fuzzy Logic – Based Systems with learning System Based on GAs. The systems applying these design approaches have received the general name of Genetic Fuzzy Systems (GFSs). The purpose of this paper is to present a comparative study between the Knowledge Bases (KBs) obtained with a GFS that use the “standard” probabilities of applying genetic operators and the KBs obtained from a GFS that use the probabilities that have been obtained previously from the Additional Genetic System (AGS). Key-Words: Fuzzy rules, learning, genetic algorithms, Fuzzy logic, Fuzzy logic based system.
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