Generative Design

Generative art: a practice in constant change

“Floating Lines in the DeepSpace”. A generative artwork by Miguel Neto & Rodrigo Carvalho.

To “generate” — as described by the Merriam-Webster dictionary — is to define or originate (something, such as a mathematical or linguistic set or structure) by the application of one or more rules or operations.

For generations, artists and scientists have helped reshaping this term into an abstraction:

Generative art takes place in a structured system – such as a set of natural language rules, a computer program, a machine, or other procedural inventions¹ – created by the artist and aimed at producing multiple, and potentially endless, results from the manipulation of an initial form.

[When we talk about generative art], the term (generative) is simply a reference to how the art is made, and it makes no claims as to why the art is made this way or what its content is.¹


Despite its modern approach, generative art is “as old as art itself”¹. Since Wolfgang Amadeus Mozart, and throughout history, artists have designed complex and simple systems — as in the works of Elsworth Kelly or John Cage — for the creation of new generative artworks.

Set aside Computer Science and AI, several art practices have contributed to the development of generative art. These include Electronic Music, Computer Graphics, Animation, VJ Culture, Industrial Design, and Architecture.

The youth culture and audiovisual artists, in particular, are bringing generative art to the eye of the media as no one has ever done before.

To bring generative art to a club night is to expose and showcase the potential of such practice to a massive crowd. Max Cooper, Alva Noto, Ryoichi Sakamoto, Squarepusher, and many other A/V artists are currently basing most of their work and live shows on machine generated art.


Complexity science is a relatively young discipline aimed at understanding how the systems that rule the generative world work.

Complex systems are called so because they (typically) have a large number of small components that interact with similar nearby parts.²

Local components will interact in “nonlinear” ways, meaning that the interactions act in a non-sequential or straightforward manner. These local interactions are dynamic and in constant change, leading to the system organising itself. Scientists define these self-organising systems as complex systems.

Examples of complex systems are the human brain, Earth’s climate, living cells, the stock market, etc.


It is important to remember that complex systems may act in a chaotic manner, but never do so randomly. There is a somewhat clear distinction between chaos and randomness, especially within the field of generative art.

Philip Galanter provides us with a great example of the difference between chaos and randomness:

“…even though it is difficult to predict the specific weather 6 months from now, we can be relatively sure it won’t be 200 degrees outside, nor will we be getting 30 feet of rain on a single day, and so on. The weather exists within some minimum and maximum limits, and those expectations are a sort of container for all possible weather states.”

Generative code

There is an aspect to code which goes beyond its pure written form. Its execution constitutes what we experience.

[However], to appreciate generative code fully we need to ‘sense’ the code to build an understanding of the code’s actions. To separate the code and the resultant actions would simply limit our experience, as well as the ultimate study of these forms.²


  1. Galanter, P. What is Generative Art? — Complexity Theory as a Context for Art Theory, Available at:
  2. Cox, G., McLean, A. and Ward, A. The Aesthetics of Generative Code, Available at:

Francesco Imola is a London-based musician, weekend photographer, and current Sound Design student at the University of Greenwich.