In this paper we examine the concept of complexity as it applies to generative art and design. Complexity has many different, discipline specific definitions, such as complexity in physical systems (entropy), algorithmic measures of information complexity and the field of "complex systems". We apply a series of different complexity measures to three different generative art datasets and look at the correlations between complexity and individual aesthetic judgement by the artist (in the case of two datasets) or the physically measured complexity of 3D forms. Our results show that the degree of correlation is different for each set and measure, indicating that there is no overall "better" measure. However, specific measures do perform well on individual datasets, indicating that careful choice can increase the value of using such measures. We conclude by discussing the value of direct measures in generative and evolutionary art, reinforcing recent findings from neuroimaging and psychology which suggest human aesthetic judgement is informed by many extrinsic factors beyond the measurable properties of the object being judged.

翻译：在本文中,我们研究了复杂的概念,它适用于基因艺术和设计。复杂性有许多不同而具体的定义,例如物理系统的复杂性(昆虫化)、信息复杂性的算法计量法和“复合系统”领域。我们对三种不同的基因艺术数据集采用一系列不同的复杂度计量法,并审视艺术家(在两个数据集的情况下)的复杂度与个人审美判断的相互关系,或3D形式的物理测量复杂性。我们的结果表明,每个数据集和计量方法的关联度不同,表明没有全面的“更好”计量标准。然而,具体措施在单个数据集方面效果良好,表明谨慎选择可以增加使用这类计量措施的价值。我们最后通过讨论基因化和进化艺术的直接计量措施的价值,强化神经成像学和心理学的最新发现,表明人类审美判断来自许多超出所判断对象可测量特性的外在因素。