This simulation demonstrates the NEAT algorithm applied to evolving neural networks that can drive cars around randomly generated tracks. Each generation tests on a newly generated track to prevent overspecialization.
Neural Network Inputs: 5 distance sensors that detect track boundaries
Neural Network Outputs: Steering angle and acceleration
Fitness Function: Based on distance traveled and checkpoints passed
Population Size: 50 cars per generation