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Evolutionary Machine Learning in the Arts

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Handbook of Evolutionary Machine Learning

Part of the book series: Genetic and Evolutionary Computation ((GEVO))

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Abstract

This chapter looks at artistic and creative applications of evolutionary machine learning. While both evolutionary computing and machine learning techniques have been applied to all kinds of creative and artistic projects, it is more rare to see them used in combination. The chapter will examine the origins and uses of evolution in the arts, before presenting a case study of an evolutionary machine learning artwork. The discussion presents the technical, conceptual, and creative aspects of developing an artwork. The chapter concludes with a discussion on the rise of generative AI and how evolution might contribute to the next wave of artistic possibilities for evolutionary machine learning.

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Notes

  1. 1.

    As preparation for writing this chapter I asked both ChatGPT and Bard to provide an academic report on the applications of EML to the arts. Unfortunately, neither could provide any useful nor accurate information on this topic.

  2. 2.

    With the advent of new technological fields, such as Artificial Intelligence or Artificial Life, it is common for leading proponents to severely underestimate the difficulty in reaching goals of human-like intelligence or the capabilities of real biological life.

  3. 3.

    Lansdown was a pioneering British computer graphics and art polymath. The prize, named in his honour, is awarded by the Eurographics Association in recognition of significant contributions to computer graphics and interactivity.

  4. 4.

    For convenience I’ll use terms like ‘creature’, ‘see’, ‘hear’, ‘know’ and ‘learn’ in this section, but the reader is reminded that these terms don’t imply an anthropomorphic interpretation.

  5. 5.

    An Eden year lasts 600 Eden days, but passes by in about 10 minutes of real time.

  6. 6.

    Most child rules that mutate will not be ‘better’ than the parent rule, but, in general, the use of mutation does provide the possibility for the system to discover rules that would not be possible by crossover alone.

  7. 7.

    The name is derived from the French interpretation of ‘Goodfellow’: Bel ami.

  8. 8.

    https://twitter.com/fchollet/status/885378870848901120.

  9. 9.

    It’s worth noting that no other AI artwork has since achieved anything near a price at auction.

  10. 10.

    If there were enough images of the original kind in the training set, it might be possible to say ‘in the style of Jon McCormack’. This is left as an exercise for the reader.

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Authors and Affiliations

  1. Monash University, Melbourne, Australia

    Jon McCormack

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  1. Jon McCormack
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Correspondence to Jon McCormack .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA

    Wolfgang Banzhaf

  2. Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Penousal Machado

  3. School of Engineering and Computer Science and Centre for Data Science and Artificial Intelligence, Victoria University of Wellington, Wellington, New Zealand

    Mengjie Zhang

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McCormack, J. (2024). Evolutionary Machine Learning in the Arts. In: Banzhaf, W., Machado, P., Zhang, M. (eds) Handbook of Evolutionary Machine Learning. Genetic and Evolutionary Computation. Springer, Singapore. https://doi.org/10.1007/978-981-99-3814-8_26

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  • DOI: https://doi.org/10.1007/978-981-99-3814-8_26

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  • Online ISBN: 978-981-99-3814-8

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