Generative art is a rules-driven approach to creating artistic outputs in various mediums. For example, a fluid simulation can govern the flow of colored pixels across a digital display or a rectangle placement algorithm can yield a Mondrian-style painting. Previously, we investigated how genetic improvement, a sub-field of genetic programming, can automatically create and optimize generative art drawing programs. One challenge of applying genetic improvement to generative art is defining fitness functions and their interaction in a many-objective evolutionary algorithm such as Lexicase selection. Here, we assess the impact of each fitness function in terms of the their individual effects on generated images, characteristics of generated programs, and impact of bloat on this specific domain. Furthermore, we have added an additional fitness function that uses a classifier for mimicking a human's assessment as to whether an output is considered as "art." This classifier is trained on a dataset of input images resembling the glitch art aesthetic that we aim to create. Our experimental results show that with few fitness functions, individual generative techniques sweep across populations. Moreover, we found that compositions tended to be driven by one technique with our current fitness functions. Lastly, we show that our classifier is best suited for filtering out noisy images, ideally leading towards more outputs relevant to user preference.

翻译：生成艺术是一种基于规则的方法，用于在各种媒介中创作艺术输出。例如，流体模拟可以控制彩色像素在数字显示屏上的流动，矩形放置算法可以生成蒙德里安风格的绘画。先前，我们研究了遗传改进——遗传编程的一个子领域——如何自动创建和优化生成艺术绘图程序。将遗传改进应用于生成艺术的一个挑战在于定义适应度函数及其在诸如词典选择等多目标进化算法中的交互作用。在此，我们评估了每个适应度函数在生成图像上的个体效应、生成程序的特征以及代码膨胀在这一特定领域中的影响。此外，我们增加了一个额外的适应度函数，该函数使用分类器来模拟人类对于输出是否被视为“艺术”的判断。该分类器在一个类似于我们旨在创建的故障艺术美学的输入图像数据集上进行训练。我们的实验结果表明，在适应度函数较少的情况下，个体生成技术会在种群中全面扩散。此外，我们发现，在我们当前的适应度函数下，作品构成往往由一种技术主导。最后，我们证明我们的分类器最适合用于过滤噪声图像，从而理想地导向更符合用户偏好的输出。