Live, in-browser GA demos you can run without writing code.
A tour of the in-browser genetic-algorithm demos and how to run them.
A population of random vehicles tries to roll across uneven terrain. The GA evolves better cars over time.
A delivery drone finds a short tour through ten random stops on a 2D map. The GA evolves the visit order.
A genetic algorithm evolves a fixed plan of moves that threads a fresh perfect maze from the start corner to the exit.
Six antennas on a 2D map with obstacles. The GA evolves their positions to maximise coverage and limit wasted overlap.
A rocket falls with random drift. The GA evolves a thrust and gimbal program that lands it softly on the pad.
Fifteen jointed bipeds try to cross a rigid plank road full of holes and swinging wrecking balls. The GA evolves both the gait and the body shape so the right build survives.
Steer a spacecraft to four deep-space beacons through a four-body gravity field using a fresh genetic algorithm at every decision point.
A car learns to steer around a race track using five distance sensors. The genetic algorithm evolves a tiny neural network from crashing into walls to completing clean laps.
A dino learns to play an endless runner, jumping cacti and ducking flying obstacles. The genetic algorithm evolves a tiny neural network from tripping at the first hurdle to long clean runs.
A paddle learns to keep a bouncing ball in play and smash a wall of bricks. The genetic algorithm evolves a tiny neural network from missing the ball to long rallies.
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