Ant Foraging & Predator Simulation

Ants evolve to find food while avoiding predators, and predators evolve to hunt them — two NEAT populations co-evolving in one arena.

Generation0

Best Prey Fitness0

Best Predator Fitness0

Alive Prey0

Alive Predators0

Food Found0

Prey Caught0

Prey Wins0

Predator Wins0

Simulation Speed: 1x

Simulation Parameters

Prey Settings

Prey Population150

Max Lifetime200

Energy From Food200

Food Count4

Prey Max Speed3

Turn Rate0.2

Show sensors

Predator Settings

Enable Predators

Predator Population50

Predator Max Lifetime300

Energy From Prey200

Predator Max Speed2.5

Show sensors

About This Simulation

This simulation demonstrates ants (prey) learning to find food while avoiding predators via the NEAT algorithm. The neural networks evolve over generations to develop foraging and hunting strategies.

Technical Details

Prey Neural Network: Uses food, obstacle, and predator sensors to control movement
Predator Neural Network: Uses prey and obstacle sensors to optimize hunting strategies
Settings: Prey settings are applied immediately, predator settings affect the next generation
Predators: Are off by default – when disabled they won't be added to the next generation, but their evolution state is saved