Neural networks, evolutionary algorithms.

The idea of evolving artificial networks by evolutionary algorithms is based on a powerful metaphor: the evolution of the human brain. The application of evolutionary algorithms to neural network optimization is an active field of study. The success and speed of training of neural network is based on the initial parameter settings, such as architecture, initial weights, learning rates, and others. A lot of research is being done on how to find the optimal network architecture and parameter settings given the problem it has to learn. One possible solution is use of evolutionary algorithms to neural network optimization systems. We can distinguish two separate issues for it: on the one hand weight training, and on the other hand architecture optimization. Next, we will focus on the architecture optimization and especially on the comparison of different strategies of neural network architecture encoding for the purchase of the evolutionary algorithm.