Genetic algorithms "learn" to make better decisions by making continuous improvements in strategy based the fitness of that solution for survival. These algorithms are modeled after Darwinian evolution, using random variations in methodology to improve their fitness.

In this talk we walk through an example of how to create a genetic algorithm to learn the best strategy for playing a board game. We'll define the DNA of the game and look at what parameters control how the solution evolves and improves.