# Genetic Algorithms & Evolution

**Track:** Physics, Motion & Emergence — Creative Coding — the existing 50
**Framework / surface:** p5.js
**Level:** Advanced
**Prerequisites:** Arrays of Objects, Complexity Intuition

**In one line:** Evolve solutions by fitness, selection, and mutation.

## Theory, aesthetics & inspiration

A genetic algorithm borrows Darwin's logic as a search method: encode candidate solutions as genomes, score each by a fitness function, then breed the best through crossover and mutation, letting successive generations climb toward what works. John Holland formalized the approach in "Adaptation in Natural and Artificial Systems" (1975), framing evolution as a parallel exploration of possibility. Karl Sims gave it spectacular embodiment in "Evolved Virtual Creatures" (1994), where simulated bodies and brains evolved to swim, walk, and compete—forms no designer authored. The aesthetic is discovery without a designer: solutions that look purposeful yet were found, not drawn, often arriving by strange and unintuitive routes.