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.

中文翻译：

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复杂之谜

在本文中，我们研究了适用于生成艺术和设计的复杂性概念。复杂度具有许多不同的，学科特定的定义，例如物理系统的复杂度（熵），信息复杂度的算法度量以及“复杂系统”的领域。我们对三个不同的生成艺术数据集应用了一系列不同的复杂性度量，并研究了复杂性与艺术家（在两个数据集的情况下）个人审美判断之间的相关性或物理测量的3D形式的复杂性。我们的结果表明，每个集合和度量的相关程度不同，表明没有总体的“更好”的度量。但是，特定的度量在单个数据集上确实表现良好，表明谨慎选择可以增加使用此类措施的价值。最后，我们讨论了直接量度在生成和进化艺术中的价值，并加强了神经影像学和心理学的最新发现，这些发现表明，人类审美判断是受许多外部因素（被测物体的可测量特性）提供的。