He Cheng, Huang Shihua, Cheng Ran, Tan Kay Chen, Jin Yaochu
IEEE Trans Cybern. 2021 Jun;51(6):3129-3142. doi: 10.1109/TCYB.2020.2985081. Epub 2021 May 18.
Recently, increasing works have been 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 multiobjective evolutionary algorithm driven by the generative adversarial networks (GANs). At each generation of the proposed algorithm, the parent solutions are first classified into real and 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 ten benchmark problems with up to 200 decision variables. The experimental results on these test problems demonstrate the effectiveness of the proposed algorithm.
最近，人们提出了越来越多的工作来使用机器学习模型驱动进化算法。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。由于模型训练通常需要一定数量的数据（即算法生成的候选解），由于维度诅咒，随着问题规模的增加，性能会迅速恶化。为了解决这个问题，我们提出了一种由生成对抗网络（GAN）驱动的多目标进化算法。在该算法的每一代中，首先将父代解分类为真实样本和虚假样本以训练GAN；然后由训练好的GAN对后代解进行采样。由于GAN强大的生成能力，我们提出的算法能够在有限的训练数据下在高维决策空间中生成有希望的后代解。该算法在具有多达200个决策变量的十个基准问题上进行了测试。这些测试问题的实验结果证明了该算法的有效性。
