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.
翻译:黑盒优化( BBO) 算法( BBO) 涉及寻找解决缺失分析细节问题的最佳办法。 这些问题的典型方法大多基于坚固且固定的 emph{ a siti} 假设, 如高森分布。 但是, 复杂的现实世界问题远非 emph{ a siti} 分布, 给这些方法带来一些意想不到的障碍 。 在本文中, 我们提出了一个优化工具, 使用基因对抗网( OPT- GAN) 来指导黑盒问题的搜索, 通过估计Popima 的分布。 这种方法可以了解选择对象所支配的最佳区域的广泛分布 。 实验显示 OTP- GAN 与其他经典的 BBO 算法相比, 特别是高斯 假设的算法 。