The Enigma of Complexity
This research addresses the challenge of objectively quantifying aesthetic value in generative art, which is a problem for artists and designers seeking to understand and optimize their creations.
This paper investigates the correlation between various complexity measures and aesthetic judgment in generative art across three datasets. It finds that no single complexity measure consistently correlates with aesthetic judgment, but specific measures perform well on individual datasets.
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.