The Enigma of Complexity

TL;DR

This paper explores various complexity measures applied to generative art datasets, revealing that no single measure universally correlates with aesthetic judgment, but specific measures can be effective when carefully chosen.

Contribution

It systematically compares multiple complexity measures across datasets, highlighting the importance of selecting appropriate measures for aesthetic evaluation in generative art.

Findings

Different complexity measures correlate variably with aesthetic judgment.

No single complexity measure is universally superior across datasets.

Careful selection of measures enhances the evaluation of generative art complexity.

Abstract

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.