  Black-box optimization (BBO) algorithms are concerned with finding the best
solutions for the problems with missing analytical details. Most classical
methods for such problems are based on strong and fixed \emph{a priori}
assumptions such as Gaussian distribution. However, lots of complex real-world
problems are far from the \emph{a priori} distribution, bringing some
unexpected obstacles to these methods. In this paper, we present an optimizer
using generative adversarial nets (OPT-GAN) to guide search on black-box
problems via estimating the distribution of optima. The method learns the
extensive distribution of the optimal region dominated by selective candidates.
Experiments demonstrate that OPT-GAN outperforms other classical BBO
algorithms, in particular the ones with Gaussian assumptions.
