  Deep learning techniques, such as Deep Boltzmann Machines (DBMs), have
received considerable attention over the past years due to the outstanding
results concerning a variable range of domains. One of the main shortcomings of
these techniques involves the choice of their hyperparameters, since they have
a significant impact on the final results. This work addresses the issue of
fine-tuning hyperparameters of Deep Boltzmann Machines using metaheuristic
optimization techniques with different backgrounds, such as swarm intelligence,
memory- and evolutionary-based approaches. Experiments conducted in three
public datasets for binary image reconstruction showed that metaheuristic
techniques can obtain reasonable results.
