In 1968, a computer scientist named A. Michael Noll exhibited a set of prints at the Howard Wise Gallery in New York. The images were composed of mathematically generated lines and curves, plotted by an IBM 7094 computer and drawn by a Stromberg-Carlson microfilm plotter. Most visitors assumed they were looking at abstract drawings by a human artist. When told that a computer had made them, reactions ranged from fascination to outrage. Could a machine really make art? The question that Noll provoked over half a century ago is more relevant than ever — because generative art has evolved from a niche experiment into one of the most vibrant and rapidly growing areas of contemporary art.
Generative art is art created by a system that operates with some degree of autonomy. Typically, an artist writes a set of rules — an algorithm — and then sets it in motion. The algorithm, often incorporating elements of randomness, produces the artwork. The artist designs the system; the system creates the individual outputs. Each output is unique, yet all share the visual DNA encoded in the algorithm. It is like designing a garden: you choose the plants, plan the layout, prepare the soil — but the specific way each flower blooms is beyond your control.
This article explores the history of generative art, explains how it works, examines its relationship to traditional art, and looks at how platforms like Art Blocks have brought it to a new audience.
What Is Generative Art?
Generative art is any art practice where the artist creates a process — a set of rules, a computer program, a mechanical system, or a chemical reaction — that generates the artwork with some degree of autonomy. The artist does not directly draw each line or place each color. Instead, they design a system that does.
The "generative" element can come from many sources:
Computer algorithms — The most common form today. Artists write code that produces visual output based on mathematical functions, random number generators, and logical rules.
Mathematical systems — Fractals, cellular automata, Fibonacci sequences, and other mathematical structures can generate complex visual patterns.
Physical processes — Wind, gravity, chemical reactions, and biological growth can serve as generative systems. Jackson Pollock's drip paintings are sometimes cited as analog generative art — the paint's trajectory was influenced by gravity, viscosity, and gesture in ways the artist could not fully predict.
Artificial intelligence — Machine learning models trained on image datasets can generate new images, though this overlaps with the separate (and controversial) field of AI art.
The key distinction is between the artist as author of the system versus the artist as author of the output. In generative art, the artist designs the rules; the outputs emerge from those rules, often surprising even their creator.
A Brief History of Generative Art
Early Computer Art (1960s)
The earliest computer-generated artworks appeared in the early 1960s, created by scientists and engineers who had access to mainframe computers. Key pioneers include:
Frieder Nake (Germany) — Created algorithmic drawings using a plotter attached to a computer, producing works that explored the intersection of mathematical precision and aesthetic beauty.
Georg Nees (Germany) — Exhibited some of the first computer-generated art in 1965, using algorithms to produce compositions based on controlled randomness.
Vera Molnár (France/Hungary) — One of the first women in computer art, Molnár used algorithms to create variations on geometric themes, exploring how systematic rule-breaking creates visual interest. She worked well into her nineties and became a celebrated figure in the generative art revival.
Harold Cohen (UK/US) — Created AARON, an autonomous drawing program that he developed continuously from the 1970s until his death in 2016. AARON could independently compose and color original drawings, making it one of the longest-running projects in generative art history.
The Processing Era (2001–Present)
The creation of Processing in 2001 by Casey Reas and Ben Fry — a free, open-source programming language designed specifically for visual artists — democratized generative art. Suddenly, artists did not need access to mainframe computers or computer science degrees. Processing (and later p5.js, its JavaScript-based sibling) provided an accessible entry point for artists interested in creative coding.
Processing inspired a generation of artists including:
Casey Reas — Co-creator of Processing, whose own generative works explore emergent complexity arising from simple rules.
Joshua Davis — Created explosive, colorful generative compositions that influenced graphic design and advertising.
Zach Lieberman — Co-founded openFrameworks and created interactive generative installations that respond to human movement and gesture.
Generative art uses algorithms and code to create visual compositions where each output is unique, balancing mathematical precision with controlled randomness. Photo by Milad Fakurian on Unsplash
Art Blocks and the NFT Revolution
In 2020, artist and developer Erick Calderon launched Art Blocks, a platform that combined generative art with blockchain technology. Artists uploaded their algorithms to the Ethereum blockchain, and collectors "minted" unique outputs generated by the code at the moment of purchase. Each minted piece was mathematically unique — generated by a unique random seed — yet visually related to all other outputs from the same algorithm.
Art Blocks brought generative art to an entirely new audience. Projects like Tyler Hobbs's "Fidenza," Dmitri Cherniak's "Ringers," and Matt DesLauriers's "Meridian" demonstrated that code-based art could produce works of genuine aesthetic beauty and conceptual depth. The platform also created a new economic model for generative artists, who earned royalties on both primary sales and secondary market transactions.
How Generative Art Works
At its core, most generative art follows a similar process:
The artist writes an algorithm — A set of coded instructions that defines the visual parameters: what shapes to draw, what colors to use, how elements should be arranged, what transformations to apply.
Randomness is introduced — Random number generators (or pseudo-random seeds) introduce variation. The algorithm might randomly select colors from a curated palette, randomly position elements within defined areas, or randomly vary the size and rotation of shapes.
The algorithm executes — The code runs, producing a visual output. Each execution with a different random seed produces a different result.
The artist curates — Most generative artists run their algorithms thousands of times, selecting the outputs that best represent their vision. The curatorial eye remains essential — the algorithm provides the raw material, but the artist decides what is good.
The artistic skill in generative art lies in designing systems that produce outputs that are both varied (each one different) and coherent (all recognizably from the same algorithm). It requires understanding of mathematics, color theory, composition, and programming — a genuinely interdisciplinary practice.
Generative Art and Traditional Art
Critics sometimes dismiss generative art as "just computer output" rather than "real art." This criticism misunderstands the creative process involved. Writing an algorithm that produces beautiful, compelling visual output requires the same aesthetic judgment, cultural knowledge, and creative vision that any other art form demands. The tool is different — code instead of a brush — but the artistic decisions are equally intentional.
In fact, generative art connects to a long tradition of rule-based art making. Islamic geometric patterns, which use mathematical rules to produce infinitely complex tiling patterns, are generative art in everything but name. Sol LeWitt's wall drawings, which consist of written instructions executed by others, are conceptually identical to generative algorithms. Even the fundamental elements of visual art — line, shape, color, pattern — can be understood as parameters in a generative system.
Getting Started with Generative Art
If you are interested in exploring generative art, either as a creator or an appreciator:
Try p5.js — This free, browser-based JavaScript library makes it easy to start creating generative art with no prior programming experience. The official tutorials are excellent.
Explore Art Blocks and fxhash — Browse curated generative art collections to develop your visual literacy. Pay attention to how different algorithms produce different aesthetic qualities.
Visit museum collections — MoMA, the Whitney, the Centre Pompidou, and the ZKM in Karlsruhe all have significant computer art holdings.
Read "The Nature of Code" by Daniel Shiffman — A free online textbook that teaches programming concepts through creative coding exercises.
Join the community — The creative coding community is exceptionally welcoming. Platforms like OpenProcessing, the Generative Artists Club, and creative coding Discord servers offer support and inspiration.
Final Thoughts
Generative art sits at a fascinating intersection of creativity, mathematics, and technology. It challenges traditional notions of authorship (who made this — the artist or the algorithm?), originality (if an algorithm can produce infinite variations, what makes any single one special?), and skill (is writing code a legitimate artistic skill?). These questions do not have easy answers, but asking them pushes our understanding of what art is and what it can be.
What makes the best generative art compelling is the same thing that makes any art compelling: someone cared deeply about how it looks and what it communicates. The medium is code. The output is visual. But the driving force is the same human impulse to create beauty, explore complexity, and share a unique way of seeing the world.
Want to explore more about the intersection of art and technology? Read about digital art as a creative frontier, or discover how NFTs changed the art market.
