Comparison between artificial neural net and pseudo neural net classification in iris dataset case

Abstract

This research deals with a novel approach to classification. This paper deals with a synthesis of a complex structure, which serves as a classifier. This structure is similar to classical artificial neural net and therefore a comparison with them is performed. The proposed method for classifier structure synthesis utilizes Analytic Programming (AP) as the tool of the evolutionary symbolic regression. AP synthesizes a whole structure of the relation between inputs and output. Classical artificial neural networks, where a relation between inputs and outputs is based on the mathematical transfer functions and optimized numerical weights, were an inspiration for this work. Iris data (a known benchmark for classifiers) was used for testing of the proposed method. For experimentation, Differential Evolution for the main procedure and also for meta-evolution version of analytic programming was used.