Generative Adversarial Networks (GANs) are very useful methods to address semi-supervised learning (SSL) datasets, thanks to their ability to generate samples similar to real data. This approach, called SSL-GAN has attracted many researchers in the last decade. Evolutionary algorithms have been used to guide the evolution and training of SSL-GANs with great success. In particular, several co-evolutionary approaches have been applied where the two networks of a GAN (the generator and the discriminator) are evolved in separate populations. The co-evolutionary approaches published to date assume some spatial structure of the populations, based on the ideas of cellular evolutionary algorithms. They also create one single individual per generation and follow a generational replacement strategy in the evolution. In this paper, we re-consider those algorithmic design decisions and propose a new co-evolutionary approach, called Co-evolutionary Elitist SSL-GAN (CE-SSLGAN), with panmictic population, elitist replacement, and more than one individual in the offspring. We evaluate the performance of our proposed method using three standard benchmark datasets. The results show that creating more than one offspring per population and using elitism improves the results in comparison with a classical SSL-GAN.

本研究旨在解决现有协同进化半监督学习生成对抗网络（SSL-GAN）的局限性，尤其是其每代仅生成一个个体的问题。提出了一种新的协同进化方法，即协同精英SSL-GAN（CE-SSLGAN），通过采用泛米克种群、精英替代策略以及生成多个子代来提升性能。实验结果表明，该方法在三个标准基准数据集上均显示出优于传统SSL-GAN的效果。

在协同进化半监督学习GAN中生成多个子代