Genetic Programming

We’ve released a genetic programming simulation that showcases using a genetic algorithm to evolve solutions to an optimization problem.

The simulation is made up of four key behaviors:

  • fitness.py: Calculates a fitness score for a potential solution.
  • evaluate.py: Compares and determines the best fitness score among the solutions.
  • crossover.py: Create new solution options from the existing solutions.
  • mutate.py: Randomly introduce changes in the agents.

When added to a pool of agents the behaviors will converge to the optimal solution. Read more about genetic programming in our accompanying blog post.