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

OPT-GAN: A Broad-Spectrum Global Optimizer for Black-box Problems by Learning Distribution