Mutation Control in Neat for Customizable Neural Networks

Abstract

A modification of the NEAT algorithm is proposed through the use of a mechanism for changing the level of mutations. At the same time, both the weight coefficients and the number of neurons in the hidden layers and the connections between them change. For the task of modelling the XOR operator, a significant acceleration of the training of a neural network was obtained.