Robbie the Robot - Genetic Algorithm Evolution

Population Size: 20,000 robots per generation
Genome: Each robot has a simple genome that matches a possible state (the presence or absence of a can or wall at each of the four cardinal directions, plus the present square) with an action (move in a cardinal direction, move randomly (but safely), or attempt to take the can at the present square)
Fitness Function: Robots are scored based on cans collected during their lifetime
Selection: Top performers are selected for reproduction with mutation
Environment: 10×10 grid world with 50 randomly distributed cans
Lifespan: Each robot gets 200 moves (2 × grid area) to collect as many cans as possible
Implementation: Uses Web Workers for non-blocking genetic algorithm computation

Watch as a population of robots evolves using genetic algorithms to efficiently collect cans in a grid world. Each robot is controlled by a simple genome - the best performers pass their "genes" to the next generation, while poor performers are eliminated. Over hundreds of generations, you'll see increasingly intelligent behavior emerge.

What you're seeing: The visualization shows the best robot from the current generation navigating a 10×10 grid world containing 50 randomly placed cans. The genetic algorithm runs in a background worker thread with a population of 20,000 robots per generation.