Optimizing Deep Neural Network Architecture: A Tabu Search Based Approach

Understand

The performance of Feedforward neural network (FNN) fully de-pends upon the selection of architecture and training algorithm.

• FNN architecture can be tweaked using several parameters, such as the number of hidden layers, number of hidden neurons at each hidden layer and number of connections between layers.
• There may be exponential combinations for these architectural attributes which may be unmanageable manually, so it requires an algorithm which can automatically design an optimal architecture with high generalization ability.
• Numerous optimization algorithms have been utilized for FNN architecture determination.