generative-art

name	generative-art
description	Use when creating algorithmic or generative visual art. Covers composition through code, controlled randomness, colour systems, and building work that is varied without being arbitrary.
metadata	{"category":"design","version":"1.0.0","tags":["generative","algorithmic-art","creative-coding","randomness","canvas"]}

Generative Art

Purpose

Create visual work through code, where the interesting output comes from constrained randomness rather than unconstrained noise. Pure randomness produces mush; the craft is in the constraints.

When to Use

• Creating algorithmic or procedural visual work.
• Building a generative system that produces a family of related outputs.
• Data-driven visual work.
• Exploring composition computationally.

Capabilities

• Controlled randomness: distributions, noise fields, seeded reproducibility.
• Composition: grids, subdivision, packing, flow fields.
• Colour systems for generative palettes.
• Output at print resolution.

Inputs

• The visual idea or the underlying system.
• Constraints: palette, format, medium.
• Whether outputs must be reproducible (they should be).

Outputs

• A system producing a family of related, non-identical works.
• Seeded, reproducible output.
• Assets at the resolution the medium requires.

Workflow

1. Seed the randomness — Always. An unseeded generative system produces something beautiful once and can never produce it again. This is the first thing to get right and the most commonly skipped.
2. Constrain the space — Randomness within a structure. A grid whose cells vary, a palette whose members are sampled, an angle that jitters within a range. Unconstrained randomness is visual noise.
3. Use noise, not uniform random, for anything spatial — Perlin or simplex noise produces coherent variation. Math.random() per pixel produces static.
4. Build the palette as a system — Sample from a small set, or from a ramp. Per-element random colour destroys any coherence the composition had.
5. Generate many, select few — The output of a generative system is a distribution. Produce a hundred, choose the ones that work, and adjust the constraints toward them.
6. Render at the output resolution — Not scaled up afterwards. A generative composition designed at 800px and upscaled to print looks exactly like that.

Best Practices

• An unseeded system is a system that cannot be reproduced, iterated on, or printed at a different size. Seed it from the start.
• Perlin and simplex noise are what make generative work look organic rather than random. The difference between random() and noise(x, y) is the difference between static and a landscape.
• Constrain the colour: sample from a palette of four, or from a single ramp. Fully random colour is the most reliable way to make generative work look amateurish.
• Symmetry and repetition give the eye something to hold. Pure variation with no structure is exhausting to look at.
• The best generative systems have one or two parameters that meaningfully change the output. A system with forty knobs is unexplorable.
• Produce original systems. Do not reimplement a recognizable artist's signature work.

Examples

Seeded, constrained, and using noise for coherence:

import { createNoise2D } from "simplex-noise";
import Alea from "alea";

// Seeded: this exact image can be regenerated, at any resolution, forever.
const SEED = "2026-07-14-a";
const prng = new Alea(SEED);
const noise2D = createNoise2D(prng);

// A palette, sampled — not random colour per element.
const PALETTE = ["#0f172a", "#1e40af", "#0891b2", "#f59e0b"];
const PAPER = "#faf8f3";

function draw(ctx, width, height) {
  ctx.fillStyle = PAPER;
  ctx.fillRect(0, 0, width, height);

  const GRID = 48;
  const cell = width / GRID;

  for (let x = 0; x < GRID; x++) {
    for (let y = 0; y < GRID; y++) {
      // Noise, not random: adjacent cells are RELATED, which is what makes
      // the field read as a structure rather than as static.
      const n = noise2D(x * 0.06, y * 0.06);          // -1..1, spatially coherent
      const angle = n * Math.PI;                       // a flow field
      const length = cell * (0.4 + Math.abs(n) * 0.9); // varies with the field

      // Colour is sampled from the palette, indexed by the field — so colour
      // varies coherently too, rather than speckling.
      const colour = PALETTE[Math.floor(((n + 1) / 2) * PALETTE.length * 0.999)];

      // Sparse: skip cells where the field is weak. Negative space is composition.
      if (Math.abs(n) < 0.18) continue;

      const cx = x * cell + cell / 2;
      const cy = y * cell + cell / 2;

      ctx.save();
      ctx.translate(cx, cy);
      ctx.rotate(angle);
      ctx.strokeStyle = colour;
      ctx.lineWidth = cell * 0.14;
      ctx.lineCap = "round";
      ctx.beginPath();
      ctx.moveTo(-length / 2, 0);
      ctx.lineTo(length / 2, 0);
      ctx.stroke();
      ctx.restore();
    }
  }
}

// Print: render at the final resolution, not upscaled.
// A2 at 300 DPI = 4961 x 7016 px. The grid and line widths scale with `width`,
// so the same seed produces the same composition at any size.

Notes

• The line if (Math.abs(n) < 0.18) continue; is doing a disproportionate amount of the compositional work: it creates negative space where the field is weak, which gives the piece structure. A generative system that fills every cell is almost always worse than one that does not.
• Seeded PRNGs (Alea, and similar) are essential. Math.random() cannot be seeded in JavaScript, which makes any system built on it irreproducible.
• Generate in batches and curate. The system's output is a distribution; your job is to shape the distribution and then choose from it.