  Black-box optimization (BBO) algorithms are concerned with finding the best
solutions for problems with missing analytical details. Most classical methods
for such problems are based on strong and fixed a priori assumptions, such as
Gaussianity. However, the complex real-world problems, especially when the
global optimum is desired, could be very far from the a priori assumptions
because of their diversities, causing unexpected obstacles. In this study, we
propose a generative adversarial net-based broad-spectrum global optimizer
(OPT-GAN) which estimates the distribution of optimum gradually, with
strategies to balance exploration-exploitation trade-off. It has potential to
better adapt to the regularity and structure of diversified landscapes than
other methods with fixed prior, e.g., Gaussian assumption or separability.
Experiments on diverse BBO benchmarks and high dimensional real world
applications exhibit that OPT-GAN outperforms other traditional and neural
net-based BBO algorithms.
