@article{2392,
  author = {Kaoutar Senhaji, Hassan Ramchoun, Mohamed Ettaouil},
  title = {Multilayer Perceptron: NSGA II for a New Multi-Objective Learning Method for Training and Model Complexity},
  journal = {Journal of Electronic Systems},
  year = {2017},
  volume = {7},
  number = {4},
  doi = {},
  url = {http://www.dline.info/jes/fulltext/v7n4/jesv7n4_1.pdf},
  abstract = {The multi-layer perceptron has proved its efficiencies in several fields as pattern and voice recognition. Unfortunately,
the classical training for MLP suffers from a poor generalization. In this respect, we have proposed a new multiobjective
training model with constraints which satisfies two objectives. The first one is the learning objective: minimizing the perceptron
error and the second is the complexity objective: optimizing number of weights and neurons. The proposed model will provide
a balance between the multi-layer perceptron learning and the complexity to get a good generalization. Our model has been
solved using an evolutionary approach called the Non-Dominated Sorting Genetic Algorithm (NSGA II). This approach has
led to a good representation of the Pareto set for the MLP network, from which an improved generalization performance model
is selected.},
}