  Estimation of Distribution Algorithms (EDAs) require flexible probability
models that can be efficiently learned and sampled. Deep Boltzmann Machines
(DBMs) are generative neural networks with these desired properties. We
integrate a DBM into an EDA and evaluate the performance of this system in
solving combinatorial optimization problems with a single objective. We compare
the results to the Bayesian Optimization Algorithm. The performance of DBM-EDA
was superior to BOA for difficult additively decomposable functions, i.e.,
concatenated deceptive traps of higher order. For most other benchmark
problems, DBM-EDA cannot clearly outperform BOA, or other neural network-based
EDAs. In particular, it often yields optimal solutions for a subset of the runs
(with fewer evaluations than BOA), but is unable to provide reliable
convergence to the global optimum competitively. At the same time, the model
building process is computationally more expensive than that of other EDAs
using probabilistic models from the neural network family, such as DAE-EDA.
