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International Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar)

EvoMUSART 2021: Artificial Intelligence in Music, Sound, Art and Design pp 203–217Cite as

The Enigma of Complexity

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12693))

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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Notes

  1. 1.

    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].

  2. 2.

    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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Author information

Authors and Affiliations

  1. SensiLab, Monash University, Melbourne, Australia

    Jon McCormack & Camilo Cruz Gambardella

  2. Goldsmiths, University of London, London, UK

    Andy Lomas

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  1. Jon McCormack
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  2. Camilo Cruz Gambardella
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  3. Andy Lomas
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Correspondence to Jon McCormack .

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Editors and Affiliations

  1. University of A Coruña, A Coruña, Spain

    Juan Romero

  2. University of Coimbra, Coimbra, Portugal

    Tiago Martins

  3. University of A Coruña, A Coruña, Spain

    Nereida Rodríguez-Fernández

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McCormack, J., Cruz Gambardella, C., Lomas, A. (2021). The Enigma of Complexity. In: Romero, J., Martins, T., Rodríguez-Fernández, N. (eds) Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2021. Lecture Notes in Computer Science(), vol 12693. Springer, Cham. https://doi.org/10.1007/978-3-030-72914-1_14

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  • DOI: https://doi.org/10.1007/978-3-030-72914-1_14

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  • Online ISBN: 978-3-030-72914-1

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