Selection pressure and an efficiency of neural network architecture evolving

Abstract

The success of artificial neural network evolution is determined by many factors. One of them is the fitness function used in genetic algorithm. Fitness function determines selection pressure and therefore influences the direction of evolution. It decides, whether received artificial neural network will be able to fulfill its tasks. Three fitness functions are proposed and examined in the paper, every one of them gives different selection pressure. Comparison and discussion of obtained results for every function is made.