Recently, more and more works have proposed to drive evolutionary algorithms using machine learning models.Usually, the performance of such model based evolutionary algorithms is highly dependent on the training qualities of the adopted models.Since it usually requires a certain amount of data (i.e. the candidate solutions generated by the algorithms) for model training, the performance deteriorates rapidly with the increase of the problem scales, due to the curse of dimensionality.To address this issue, we propose a multi-objective evolutionary algorithm driven by the generative adversarial networks (GANs).At each generation of the proposed algorithm, the parent solutions are first classified into \emph{real} and \emph{fake} samples to train the GANs; then the offspring solutions are sampled by the trained GANs.Thanks to the powerful generative ability of the GANs, our proposed algorithm is capable of generating promising offspring solutions in high-dimensional decision space with limited training data.The proposed algorithm is tested on 10 benchmark problems with up to 200 decision variables.Experimental results on these test problems demonstrate the effectiveness of the proposed algorithm.

中文翻译：

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生成对抗网络驱动的进化多目标优化

最近，越来越多的工作提出使用机器学习模型来驱动进化算法。通常，这种基于模型的进化算法的性能高度依赖于所采用模型的训练质量。因为它通常需要一定量的数据（即算法生成的候选解）用于模型训练，由于维数灾难，性能随着问题规模的增加而迅速恶化。为了解决这个问题，我们提出了一种由生成对抗网络驱动的多目标进化算法(GANs)。在提出算法的每一代，父解首先被分类为\emph{real} 和\emph{fake} 样本来训练GANs；然后后代解决方案由经过训练的 GAN 采样。