In this paper we examine the concept of complexity as it applies to generative and evolutionary 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 evolutionary 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 generative 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 then assess the value of complexity measures for the audience by undertaking a large-scale survey on the perception of complexity and aesthetics. 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.

在本文中，我们研究了复杂性的概念，因为它适用于生成和进化的艺术和设计。复杂性有许多不同的、特定于学科的定义，例如物理系统的复杂性（熵）、信息复杂性的算法度量和“复杂系统”领域。我们将一系列不同的复杂性测量应用于三个不同的进化艺术数据集，并研究复杂性与艺术家的个人审美判断（在两个数据集的情况下）或生成 3D 形式的物理测量复杂性之间的相关性。我们的结果表明，每个集合和度量的相关程度不同，表明没有总体上“更好”的度量。然而，具体措施在个别数据集上表现良好，表明谨慎的选择可以增加使用这些措施的价值。然后，我们通过对复杂性和美学的感知进行大规模调查来评估复杂性度量对观众的价值。最后，我们讨论了直接测量在生成和进化艺术中的价值，强化了神经影像学和心理学的最新发现，这些发现表明人类审美判断受到许多外部因素的影响，而这些外部因素超出了被判断对象的可测量特性。