  Estimation of Distribution Algorithms (EDAs) require flexible probability
models that can be efficiently learned and sampled. Generative Adversarial
Networks (GAN) are generative neural networks which can be trained to
implicitly model the probability distribution of given data, and it is possible
to sample this distribution. We integrate a GAN into an EDA and evaluate the
performance of this system when solving combinatorial optimization problems
with a single objective. We use several standard benchmark problems and compare
the results to state-of-the-art multivariate EDAs. GAN-EDA doe not yield
competitive results - the GAN lacks the ability to quickly learn a good
approximation of the probability distribution. A key reason seems to be the
large amount of noise present in the first EDA generations.
