Pseudo neural networks synthesized via evolutionary symbolic regression for Pima diabetes

Abstract

This research deals with pseudo neural networks which were applied for solving Pima diabetes set. Pseudo neural networks are complex expressions synthesized by means of an evolutionary symbolic regression technique - analytic programming (AP). It represents a novel approach to classification when a relation between inputs and outputs is created. The inspiration came from classical artificial neural networks where such a relation between inputs and outputs is based on the mathematical transfer functions and optimized numerical weights. AP will synthesize a whole expression at once. There is also an advantage of suitable feature set selection during the same step of pseudo neural net synthesis. For experimentation, Differential Evolution (DE) for the main procedure and also for meta-evolution version of analytic programming (AP) was used.