On Evolving Robust Strategies for Iterated Prisoner's Dilemma

Evolution is a fundamental form of adaptation in a dynamic and complex environment. Genetic algorithms are an effective tool in the empirical study of evolution. This paper follows Axelrod's work [2] in using the genetic algorithm to evolve strategies for playing the game of Iterated Prisoner's Dilemma, using co-evolution, where each member of the population (each strategy) is evaluated by how it performs against the other members of the current population. This creates a dynamic environment in which the algorithm is optimising to a moving target instead of the usual evaluation against some fixed set of strategies. The hope is that this will stimulate an “arms race” of innovation [3].