- author = "Santiago Garcia and Fermin Gonzalez and Luciano Sanchez",
- title = "Evolving Fuzzy Rule Based Classifiers with {GA-P}: A Grammatical Approach",
- booktitle = "Genetic Programming, Proceedings of EuroGP'99",
- year = "1999",
- editor = "Riccardo Poli and Peter Nordin and William B. Langdon and Terence C. Fogarty",
- volume = "1598",
- series = "LNCS",
- pages = "203--210",
- address = "Goteborg, Sweden",
- publisher_address = "Berlin",
- month = "26-27 " # may,
- organisation = "EvoNet",
- publisher = "Springer-Verlag",
- keywords = "genetic algorithms, genetic programming: Poster",
- ISBN = "3-540-65899-8",
-
DOI = "
doi:10.1007/3-540-48885-5_17",
-
abstract = "Genetic Programming can be used to evolve Fuzzy
Rulebased classifiers [7]. Fuzzy GP depends on a
grammar defining valid expressions of fuzzy
classifiers, and guarantees that all individuals in the
population are valid instances of it all along the
evolution process. This is accomplished by restricting
crossover and mutation so that they only take place at
points of the derivation tree representing the same
non-terminal, thus generating valid subtrees [13].
In Fuzzy GP, terminal symbols are fuzzy constants and variables that are chosen beforehand. In this work we propose a method for evolving both fuzzy membership functions of the variables and the Rule Base. Our method extends the GA-P hybrid method [6] by introducing a new grammar with two functional parts, one for the Fuzzy Rule Base (GP Part), and the other for the constants that define the shapes of the fuzzy sets involved in the Fuzzy Rule Base (GA Part). We have applied this method to some classical benchmarks taken from the collection of test data at the UCI Repository of Machine Learning Databases [9].",
-
notes = "EuroGP'99, part of \cite{poli:1999:GP}
Combination of grammar based GP and GA-P with fuzzy rules. UCI machine learning databases
First author is Santiago Garcia Carbajal",
