Generative Adversarial Networks (GANs), a generative modelling technique most commonly used for image generation, have recently been applied to the task of fine art generation. Wasserstein GANs and GANHack techniques have not been applied in GANs that generate fine art, despite their showing improved GAN results in other applications. This thesis investigates whether Wasserstein GANs and GANHack extensions to DCGANs can improve the quality of DCGAN-based fine art generation. There is also no accepted method of evaluating or comparing GANs for fine art generation. DCGAN's, Wasserstein GANs' and GANHack techniques' outputs on a modest computational budget were quantitatively and qualitatively compared to see which techniques showed improvement over DCGAN. A method for evaluating computer-generated fine art, HEART, is proposed to cover both the qualities of good human-created fine art and the shortcomings of computer-created fine art, and to include the cognitive and emotional impact as well as the visual appearance. Prominent GAN quantitative evaluation techniques were used to compare sample images these GANs produced on the MNIST, CIFAR-10 and Imagenet-1K image data sets. These results were compared with sample images these GANs produced on the above data sets, as well as on art data sets. A pilot study of HEART was performed with 20 users. Wasserstein GANs achieved higher visual quality outputs than the baseline DCGAN, as did the use of GANHacks, on all the fine art data sets and are thus recommended for use in future work on GAN-based fine art generation. The study also demonstrated that HEART can be used for the evaluation and comparison of art GANs, providing comprehensive, objective quality assessments which can be substantiated in terms of emotional and cognitive impact as well as visual appearance.