Revistes Catalanes amb Accés Obert (RACO)

Niching scheme for steady state GA-P and its application to fuzzy rule based classifiers induction

Luciano Sánchez Ramos, José Antonio Corrales González

Resum


A new method
for applying grammar based Genetic Programming
to learn fuzzy rule based classifiers
from examples is proposed. It will produce linguistically
understandable, rule based definitions
in which not all features are present
in the antecedents. A
feature selection is implicit in the
algorithm.

Since both surface and deep structure
will be learned, standard grammar based GP is
not applicable to this problem.
We have adapted GA-P algorithms, a
method formerly defined as an hybrid
between GA and GP, that is able to
perform a more effective search in
the parameters space than canonical GP do.
Our version of GA-P supports a
grammatical description of the genotype,
a syntax tree based codification
(which is more efficient than parse
tree based representations)
and a niching scheme which improves
the convergence properties of this
algorithm when applied to this problem

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