  Estimation of Distribution Algorithms (EDAs) require flexible probability
models that can be efficiently learned and sampled. Restricted Boltzmann
Machines (RBMs) are generative neural networks with these desired properties.
We integrate an RBM into an EDA and evaluate the performance of this system in
solving combinatorial optimization problems with a single objective. We assess
how the number of fitness evaluations and the CPU time scale with problem size
and with problem complexity. The results are compared to the Bayesian
Optimization Algorithm, a state-of-the-art EDA. Although RBM-EDA requires
larger population sizes and a larger number of fitness evaluations, it
outperforms BOA in terms of CPU times, in particular if the problem is large or
complex. RBM-EDA requires less time for model building than BOA. These results
highlight the potential of using generative neural networks for combinatorial
optimization.
