Complexity and Aesthetics in Generative and Evolutionary Art

TL;DR

This paper explores how different measures of complexity relate to aesthetic judgments in generative and evolutionary art, revealing that no single measure is universally best but can be effective when carefully chosen for specific datasets.

Contribution

It systematically evaluates various complexity measures against aesthetic judgments and physical complexity, highlighting the importance of dataset-specific measure selection.

Findings

Different complexity measures correlate variably with aesthetic judgments.

No single complexity measure is universally optimal across datasets.

Audience perception of complexity is influenced by extrinsic factors beyond measurable properties.

Abstract

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.