复杂性之谜
The Enigma of Complexity
摘要：
在本文中，我们研究复杂性的概念，因为它适用于生成艺术和设计。复杂性有许多不同的、学科特定的定义，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度量应用于三个不同的生成艺术数据集，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3D形式的物理测量复杂性之间的相关性。我们的研究结果表明，每个集合和度量的相关程度是不同的，这表明没有总体上“更好”的度量。然而，特定的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。最后，我们讨论的价值直接措施的生成和进化的艺术，加强最近的研究结果，从神经影像学和心理学，这表明人类的审美判断是由许多外在因素超出了可测量的属性被判断的对象。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
