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Evolving Mondrian-Style Artworks

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Computational Intelligence in Music, Sound, Art and Design (EvoMUSART 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10198))

Abstract

This paper describes a Genetic Algorithm (GA) software system for automatically generating Mondrian-style symmetries and abstract artwork. The research examines Mondrian’s paintings from 1922 through 1932 and analyses the balances, color symmetries and composition in these paintings. We used a set of eleven criteria to define the automated system. We then translated and formulized these criteria into heuristics and criteria that can be measured and used in the GA algorithm. The software includes a module that provides a range of GA parameter values for interactive selection. Despite a number of limitations, the method yielded high quality results with colors close to those of Mondrian and rectangles that did not overlap and fit the canvas.

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Author information

Authors and Affiliations

  1. Department of Software Engineering, Braude College of Engineering, Karmiel, Israel

    Miri Weiss Cohen

  2. Department of Media and Information, College of Communication Arts and Sciences, Michigan State University, East Lansing, Michigan, USA

    Leticia Cherchiglia

  3. Department of Arts, State University of Minas Gerais, UFMG, Belo Horizonte, Brazil

    Rachel Costa

Authors
  1. Miri Weiss Cohen
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  2. Leticia Cherchiglia
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  3. Rachel Costa
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Corresponding author

Correspondence to Miri Weiss Cohen .

Editor information

Editors and Affiliations

  1. University of Coimbra , Coimbra, Portugal

    João Correia

  2. RMIT University , Melbourne, Victoria, Australia

    Vic Ciesielski

  3. University of Malta , Msida, Malta

    Antonios Liapis

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Cohen, M.W., Cherchiglia, L., Costa, R. (2017). Evolving Mondrian-Style Artworks. In: Correia, J., Ciesielski, V., Liapis, A. (eds) Computational Intelligence in Music, Sound, Art and Design. EvoMUSART 2017. Lecture Notes in Computer Science(), vol 10198. Springer, Cham. https://doi.org/10.1007/978-3-319-55750-2_23

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  • DOI: https://doi.org/10.1007/978-3-319-55750-2_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55749-6

  • Online ISBN: 978-3-319-55750-2

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