Multilayer Perceptron: NSGA II for a New Multi-Objective Learning Method for Training and Model Complexity
Kaoutar Senhaji, Hassan Ramchoun, Mohamed Ettaouil
Journal of Electronic Systems, Vol. 7, No. 4, 2017.
DOI: 
Link: 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.