earticle

영어

The SSGAMLP(Small Set Genetic Algorithm Multilayer Perceptron) model helps individual evolution by group evolution. With respect to the MLP, it has better generalization, it can get unknown feature expressions of more possibilities. The model still exist many problems need to be solved. The number of nodes in the hidden layers and the population size of MLP has a great influence on the performance of SSGAMLP. So this paper focuses on the optimization of that two parameters on SSGAMLP. In this paper, the models of several different experiments are designed. By comparing the experimental data, the relationship between the parameter selection and the model performance is obtained.