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5 Genetic Fuzzy System (GFS) Strategy
In these days multifarious problem, like nonlinear optimization and machine
learning have been resolved by Genetic Algorithms (GAs) (Wermter and Sun
2000 ). A lot of Genetic Algorithm-based methods have been planed of inbreeding
fuzzy if-then states (Kasabov 1998 ).
These systems can be classi
ed into two approaches in the same manner as the
graduation non-fuzzy genetic-based machine leaning methods has 2 types.
5.1 The Michigan Approach
In the Michigan Approach (MA), only unique fuzzy rule-based system is store the
perforation of Genetic Algorithm, where as a number of fuzzy rule-based system are
saved in the Pittsburgh Approach (Hisao et al. 1999 ; Azar 2012 ). This required for
actuates to the short computation time required for federation update by genetic
steer age consequently one pro
t Michigan Approach is, Its teeny retention
necessity. But one disservice of Michigan Approach is the shortcoming of a
proximate adhesion between the transaction of Genetic Algorithm and the opti-
mization of fuzzy rule-based systems. Because the espasuration of each fuzzy rule-
based prees, hence algorithm distention for proper fuzzy if-then rules to perform its
optimization sideway.
5.2 The Pittsburg Approach
In the Pittsburg Approach a set of if-then laws is symbolic as a strand to witch
genetic inflection like crossover, mutation, selection are exerted (Hisao et al. 1999 ).
One pro
t of this approach is that performance of each fuzzy rule-based system
can be straightly used as a Fitness Function (FF). Because of population consists of
a number of fuzzy rule-based systems PA is its huge retention necessity thus it one
disservice for this approach. The Pa postulates much more retention storage than
the MA, in which a unique fuzzy rule-based system corresponds to the complete
hesitancy.
6 GFS Strategy
In subsumes systems to model categorization problems, a fuzzy if-then law or suit
consist of coef
cient (SCCs) is exert as an exclusive in GA, thus it can show the
subsumed system as a kind of Michigan Approach (Kasiri et al. 2012b ).
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