Neural networks adaptation with NEAT-like approach

Abstract

This paper describes experience with NEAT (NeuroEvolution of Augmenting Topologies) method which is based on evolutionary computation and optimization of neural networks structure and synaptic weights. Non-linear function XOR approximation is tested and evaluated with this method with the aim of perspective application in humanoid robot NAO. The experiments show that selected method NEAT is suitable for this type of adaptation of NN, because of its ability to deal with the problems which emerge in TWEAN methods. © 2010 Springer-Verlag Berlin Heidelberg.