A comparison of classification accuracy of four genetic programming-evolved intelligent structures

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.