Neural network structure optimization based on improved genetic algorithm
Wei Wu · 2012
For structural optimization of neural networks, i.e., the challenging problem to determine the number of hidden layers and the number of neurons, we propose a structural optimization algorithm based on an improved genetic algorithm (IGA). The proposed algorithm is then employed to approximate nonlinear function y=e-(x-1)2+e-(x+1)2in MATLAB. Extensive simulation demonstrates that the proposed optimization algorithm is efficient, improves adaptability and generalization ability of neural networks, and holds rapid global convergence.