Complexity and aesthetics in generative and evolutionary art

• Published in: Genetic programming and evolvable machines Vol. 23; no. 4; pp. 535 - 556
• Main Authors: McCormack, Jon, Cruz Gambardella, Camilo
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2022; Springer Nature B.V
• Subjects: Aesthetics; Artificial Intelligence; Biomedical Engineering and Bioengineering; Compilers; Complex systems; Complexity; Computer Science; Datasets; Electrical Engineering; Evolution; Interpreters; Medical imaging; Music and Design; Programming Languages; Programming Techniques; Psychology; Software Engineering/Programming and Operating Systems; Special Issue: Evolutionary Computation in Art; Evolutionary art; Fitness measure; Aesthetics; Generative art; Complexity
• ISSN: 1389-2576; 1573-7632
• DOI: 10.1007/s10710-022-09429-9

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.