Complexity and aesthetics in generative and evolutionary art

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.

Narrating global emerging issues with complexity-inspired installations

“…It is easier to engage the children in activities when playing is their motivation. [18,19] While considerable attention has been devoted to exploring aesthetic values in computational complexity [20], there remains a relative scarcity of analyses on tangible installation works. This scarcity may be attributed to the technical intricacies involved in realizing such works, resulting in a limited number of examples for study.…”

From Canvas to Code: the Evolution of Generative Art in the AI Era

“…They advocate an individual actively constructed aesthetic experience and focus on the artist's intention and emotional expression during the creative process. Such notions are supported by the research of McCormack et al who evidenced a lack of perceptual correlation between the complexity of the generated art images and aesthetics and emphasized the need to consider a wider range of factors beyond visual appearance [63]. More research in the field of cognitive science has revealed the influence of individual factors such as personality tendencies, personal history, individual relevance, specific meaning construction, and context on aesthetic judgments [64].…”

Predicting the aesthetics of dynamic generative artwork based on statistical image features: A time-dependent model

“…High-level features representing semantic information are used to examine how semantic information affects the aesthetics of photographs [ 16 , 17 ] and paintings [ 18 ]. Attempts have been made to quantify the relationships between certain image features and personal aesthetic judgments of static generative art images [ 19 , 20 ]. Effective methods for evaluating the aesthetics of dynamic generative art are still missing.…”