Complexity and aesthetics in generative and evolutionary art
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- @Article{McCormack:2022:GPEM,
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author = "Jon McCormack and Camilo {Cruz Gambardella}",
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title = "Complexity and aesthetics in generative and
evolutionary art",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2022",
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volume = "23",
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number = "4",
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pages = "535--556",
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month = dec,
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note = "Special Issue: Evolutionary Computation in Art, Music
and Design",
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keywords = "genetic algorithms, genetic programming, Complexity,
Aesthetics, Generative art, Evolutionary art, Fitness
measure",
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ISSN = "1389-2576",
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DOI = "doi:10.1007/s10710-022-09429-9",
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size = "22 pages",
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abstract = "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.",
- }
Genetic Programming entries for
Jon McCormack
Camilo Cruz Gambardella
Citations