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

Sánchez Ramos, Luciano, and Corrales González, José Antonio. "A niching scheme for steady state GA-P and its application to fuzzy rule based classifiers induction.." Mathware and Soft Computing 7.2-3 (2000): 337-350. <http://eudml.org/doc/39211>.

@article{SánchezRamos2000,
abstract = {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 sent 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.},
author = {Sánchez Ramos, Luciano, Corrales González, José Antonio},
journal = {Mathware and Soft Computing},
keywords = {Algoritmos genéticos; Programación; Conjuntos difusos; grammar based genetic programming},
language = {eng},
number = {2-3},
pages = {337-350},
title = {A niching scheme for steady state GA-P and its application to fuzzy rule based classifiers induction.},
url = {http://eudml.org/doc/39211},
volume = {7},
year = {2000},
}

TY - JOUR
AU - Sánchez Ramos, Luciano
AU - Corrales González, José Antonio
TI - A niching scheme for steady state GA-P and its application to fuzzy rule based classifiers induction.
JO - Mathware and Soft Computing
PY - 2000
VL - 7
IS - 2-3
SP - 337
EP - 350
AB - 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 sent 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.
LA - eng
KW - Algoritmos genéticos; Programación; Conjuntos difusos; grammar based genetic programming
UR - http://eudml.org/doc/39211
ER -