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The evolution of blackjack strategies

We investigate the evolution of a blackjack player. We utilise three neural networks (one for splitting, one for doubling down and one for standing/hitting) to evolve blackjack strategies. Initially a pool of randomly generated players play 1000 hands of blackjack. An evolutionary strategy is used to mutate the best networks (with the worst networks being killed). We compare the best evolved strategies to other well-known strategies and show that we can beat the play of an average casino player. We also show that we are able to learn parts of Thorpe's basic strategy.