Black-Box Optimization via Generative Adversarial Nets

• URL: http://arxiv.org/abs/2102.03888v1
• Date: Sun, 7 Feb 2021 19:12:09 GMT
• Title: Black-Box Optimization via Generative Adversarial Nets
• Authors: Minfang Lu, Fengyang Sun, Lin Wang, Bo Yang, Shuangrong Liu
• Abstract summary: We present agenerative adversarial nets (OPT-GAN) to guide search on black-box problems. Experiments demonstrate that OPT-GAN outperforms other classical BBO algorithms.
• Score: 6.46243851154653
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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