Using Genetic Algorithms to Develop Strategies for the Prisoners Dilemma

The Prisoner’s Dilemma, a simple two-person game invented by Merrill Flood & Melvin Dresher in the 1950s, has been studied extensively in Game Theory, Economics, and Political Science because it can be seen as an idealized model for real-world phenomena such as arms races (Axelrod 1984). In this paper, I describe a GA to search for strategies to play the Iterated Prisoner’s Dilemma, in which the fitness of a strategy is its average score in playing 100 games with itself and with every other member of the population. Each strategy remembers the three previous turns with a given player, by using a population of 20 strategies, fitness-proportional selection, single-point crossover with Pc=0.7, and mutation with Pm=0.001.