Learning and Evolution in Artificial Neural Networks: A Comparison Study

摘要

This paper aims at learning and evolution in artificial neural networks. Here is presented a system evolving populations of neural nets that are fully connected multilayer feed forward networks with fixed architecture solving given tasks. The system is compared with gradient descent weight training (like back propagation) and with hybrid neural network adaptation. All neural networks have the same architecture and solve the same problems to be able to be compared mutually. In order to test the efficiency of described algorithms, we applied them to the Fisher's Iris data set [1] that is the bench test database from the area of machine learning.