Abstract
We investigate the effectiveness of GP-generated
intelligent structures in classification tasks.
Specifically, we present and use four context-free
grammars to describe (1) decision trees, (2) fuzzy
rule-based systems, (3) feedforward neural networks and
(4) fuzzy Petri-nets with genetic programming. We apply
cellular encoding in order to express feedforward
neural networks and fuzzy Petri-nets with arbitrary
size and topology. The models then are examined
thoroughly in six well-known real world data sets.
Results are presented in detail and the competitive
advantages and drawbacks of the selected methodologies
are discussed, in respect to the nature of each
application domain. Conclusions are drawn on the
effectiveness and efficiency of the presented
approach.
Users
Please
log in to take part in the discussion (add own reviews or comments).