Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Evolutionary dynamics of spatial games
Proceedings of the NATO advanced research workshop and EGS topical workshop on Chaotic advection, tracer dynamics and turbulent dispersion
On Evolving Robust Strategies for Iterated Prisoner's Dilemma
AI '93/AI '94 Selected papers from the AI'93 and AI'94 Workshops on Evolutionary Computation, Process in Evolutionary Computation
Evolutionary computation: comments on the history and current state
IEEE Transactions on Evolutionary Computation
Optimal strategies of the iterated prisoner's dilemma problem for multiple conflicting objectives
IEEE Transactions on Evolutionary Computation
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This paper describes the social evolution of an environment where all individuals are repeating patterns of behaviour. The paper follows Axelrod's work [1] of computer simulations of Iterated Prisoner's Dilemma (IPD), which is widely regarded as a standard model for the evolution of cooperation. Previous studies by Axelrod [2], Hirshleifer and Coll [3], Lindgren [4], Fogel [5], Darwen and Yao [6] focused on strategies that are history dependent. In other words, these strategies use the outcome of the opponent's past game history in making a decision on a given move. This includes the most well-known strategy, tit-for-tat.The way strategies are encoded in the computer program reflects the model's assumption concerning individual decision-making. In this paper, we study environments where all players are simply repeating patterns of behaviour without using past game history. In doing so, a genetic algorithm is used to evolve such strategies in a co-evolution environment. Simulations indicate that such an environment is harmful to the evolution of cooperation.