Watch digital organisms evolve toward a goal: selection, crossover and mutation, live.

role
Solo build
status
Shipped
when
2026
ownership
Solo
Repository
gensim.evoAI research
gen 042 · select → crossover → mutate

Who did what. Personal project.

Overview

An interactive genetic-algorithm visualiser in plain HTML, CSS and JavaScript. Organisms carry genes that steer them; the fittest reproduce, mutate and improve generation by generation while you tune the parameters.

Why it exists

Evolutionary algorithms are easy to describe and hard to feel. Seeing a population learn makes selection pressure and mutation rate intuitive.

My part

  • Simulation, fitness tracking, parameter controls and post-run reports, all in vanilla JS.

How it works

  1. a

    Population

    Organisms with random genes.

  2. b

    Fitness

    Distance to the goal.

  3. c

    Select

    Fitter organisms reproduce.

  4. d

    Crossover + mutate

    A new generation.

Built with

  • JavaScript
  • HTML
  • CSS
  • Canvas

Status & next

Runs entirely in the browser.

Source: github.com/AA-KH/Genetic-Algorithm-Visualizer