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2021 | OriginalPaper | Chapter

The Enigma of Complexity

Authors : Jon McCormack, Camilo Cruz Gambardella, Andy Lomas

Published in: Artificial Intelligence in Music, Sound, Art and Design

Publisher: Springer International Publishing

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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.

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We adopted this measure as it specifically deals with complexity as defined in [22]. Machardo & Cardoso also define an aesthetic measure as the ratio of image complexity to processing complexity [20], as used by den Heijer & Eiben in their comparison of aesthetic measures [10].

Readers should not draw any direct relation between the terms “structural” and “physical” in relation to complexity used here. Structural refers to image structures, whereas physical refers to characteristics of the 3D form’s line segments.

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Title: The Enigma of Complexity
Authors: Jon McCormack
Camilo Cruz Gambardella
Andy Lomas
Publisher: Springer International Publishing
Book: Artificial Intelligence in Music, Sound, Art and Design
Print ISBN: 978-3-030-72913-4

Electronic ISBN: 978-3-030-72914-1

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-72914-1_14

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