Generative Art

2025-01-07

Generative art has been defined in many ways, but certain common characteristics emerge when analyzing these definitions. While generative art is as old as art itself and doesn’t necessarily rely on computers, its scope is undeniably broad. For the purpose of my research, I will focus specifically on generative art created through computer programs, where the system plays a key role in producing the final artwork. While researching what generative art is, I found following definitions:

BROAD DEFINITIONS:

These focus on the overarching concept of generative art—its reliance on systems and autonomy, without necessarily tying it to specific tools or processes.

  • Generative art refers to any art practice where the artist uses a system, such as a set of natural language rules, a computer program, a machine, or other procedural invention, which is set into motion with some degree of autonomy contributing to or resulting in a completed work of art.” - Philip Galanter
  • An autonomous system is one that uses a non-human decision maker, like an algorithm or some dice, to determine characteristics without direct input from a sentient creator.
  • Generative art sits at the intersection of art and mathematics, using algorithms and computational methods to produce unique visual works—and sometimes music and written pieces as well, as noted by Artland.

NARROW DEFINITIONS:

These focus on specific processes or tools involved in creating generative art, often emphasizing the use of creative coding and algorithms.

  • According to Generative artist Tyler Hobbs, “at the core of the generative process is creative coding – writing programs that generate artwork.” Generative scripts are designed so that one string of code can have multiple outputs. From there, the artist can run the script to create a series of unique artworks.
  • Generative art is an art form that relies on algorithms or systems to generate artwork, incorporating elements of randomness or structured data such as sensor inputs or API data.
  • Generative art blends the creativity of the artist with the precision and power of technology, allowing for both unpredictable and data-driven outcomes.

Taking into consideration the scope of my research and the gathered definitions, I have formulated my own understanding of generative art. In the context of my project,

  • generative art focuses on creative coding, using a computer program with a degree of autonomy. It combines a non-human decision-maker, such as a set of rules (algorithms) and elements of randomness, with the creativity of the artist to produce unique visual works.

When we can talk that something is a generative art?

Autonomy is essential in generative art. The system must operate independently, incorporating elements of randomness or semi-randomness to produce unique results. Unpredictability is a key feature of generative art. The artist designs the system but cannot fully anticipate its output, resulting in an element of surprise and discovery. Algorithms form the foundation of generative art. While tools and technologies may change over time, the underlying logic and principles of the algorithms remain constant.

As I dive deeper into the concept of generative art, it becomes clear that while some definitions provide a sense of clarity, they also raise important questions about its fundamental principles.

  • What exactly does autonomy mean in Generative Art?
  • How much control should the artist have over the system? Is it still "autonomous" if the artist adjusts parameters during the process (semi-autonomy)?
  • How can randomness be balanced with structure? What elements can be left to chance, and which need to stay fixed to maintain a consistent style?
  • What are the simplest algorithms that can create visually interesting results? How can I expand them as I learn more?

I hope that by the end of this project, I will have found answers to some of these questions, or at least gained a clearer understanding of certain aspects.

At the start of this process, I approached generative art with a very narrow understanding. I was inspired by artists sharing creations made with tools like p5.js and Processing online, which, to me, seemed like the essence of generative art. However, after exploring books like Generative Art Using Processing by Matt Pearson, Coding Art: The Four Steps to Creative Programming with the Processing Language by Yu Zhang and Mathias Funk, and Code as Creative Medium by Golan Levin and Tega Brain, my perspective has broadened significantly. These readings helped me understand the wide-ranging nature of generative art across various mediums, including music, literature, and handmade processes. While my focus remains on creating generative art through computer programming, this broader understanding provides a valuable foundation to work toward my goal of developing a generative art tool.

Foundations of Generative Art

It's worth mentioning that an inspiring moment in deepening my understanding of generative art was watching Mikhail Mansion’s talk at the Dali Museum in St. Petersburg on algorithmic art. From the very beginning, he emphasized that generative art is much more than the digital visuals we see on screens today. He explored its roots in ancient times, tracing the origins of algorithms that now form the foundation of this art form.

Mansion highlighted how generative art has been shaped over centuries, long before computers came into play. He spoke about artists from the 20th century who worked with the principles of generative art, even though their work wasn’t digitalized.

  • Ellsworth Kelly, known for his bold, abstract forms, embraced the idea of letting go of control and discovering the unexpected in art—an essential aspect of generative processes.
  • Marcel Duchamp was another pioneer, creating space for machines and random processes to take part in artistic creation, breaking away from traditional notions of authorship.
  • Ben Laposky, often considered a forerunner of digital art, generated images electronically using oscilloscopes, paving the way for future explorations.

Other artists also made groundbreaking contributions:

  • Ken Knowlton and Leon Harmon were responsible for the first ASCII art, proving that even text could serve as a canvas for creative expression.
  • Meanwhile, A. Michael Noll pushed boundaries by experimenting with computer art, digital imagery, and 3D animation. One of his fascinating experiments involved recreating Piet Mondrian’s artwork using algorithms and then asking viewers to identify the original. This experiment raised thought-provoking questions about the perception and authenticity of art.

The contributions of Vera Molnar stand out as well. A classical artist turned generative art pioneer, she used plotters to bring her mathematical calculations to life. Her fascination with geometry, particularly squares, is evident in her works, where she built layered compositions that introduced elements of randomness.

Manfred Mohr, originally an action painter inspired by Jackson Pollock, transitioned into generative art, delving into concepts like random walks—a technique rooted in chance and movement that echoes his earlier practice. Finally, Roman Verostko and Herbert W. Franke co-created the Algorists Manifesto, championing the idea that algorithms could be an intrinsic medium for artistic creation.

It was truly inspiring to explore the works of these artists who laid the groundwork for generative art, often working manually or with primitive tools. Their experiments remind us of the evolution of the field—from early, analog explorations to the digital, highly complex systems we see today. It’s fascinating to see how their ideas continue to influence and resonate with modern generative art practices, bridging the gap between art, technology, and chance.

Sources

  • Mansion, M. (2023, August 9). On Algorithmic art - creative Pinellas. Creative Pinellas. https://creativepinellas.org/magazine/algorithmic-art/
  • Generative Art: 50 Best Examples, Tools & Artists (2021 GUIDE) — AIArtists.org. (n.d.). AIArtists.org. https://aiartists.org/generative-art-design
  • Zhang, Y., & Funk, M. (2021). Coding art: The Four Steps to Creative Programming with the Processing Language. Apress.
  • What is Generative Art? (2024, October 15). https://avantarte.com/insights/guides/what-is-generative-art
  • Dmitri Cherniak - Collaborations with Avant Arte. (2024, June 1). https://avantarte.com/artists/dmitri-cherniak
  • https://library.fiveable.me/art-and-technology/unit-11/principles-generative-art-algorithmic-design/study-guide/7WzRMoPXOROCu3Hz. (n.d.).
  • Greenfield, G. (2012). Generative art: a practical guide using Processing, by Matt Pearson. Journal of Mathematics and the Arts, 6(4), 225–229. https://doi.org/10.1080/17513472.2012.742030
  • Zafeiriou, S. (2024, December 12). Generative Art for Beginners: 10 Essential Techniques | Steve Zafeiriou. Steve Zafeiriou. https://stevezafeiriou.com/generative-art-for-beginners/
  • Goodchild, A. (2024, May 22). What is Generative Art? — Amy Goodchild. Amy Goodchild. https://www.amygoodchild.com/blog/what-is-generative-art