The evolution of cooperation with different fitness functions using probabilistic cellular automata

In this work, we use probabilistic cellular automata to model a population in which the cells represent individuals that interact with their neighbors playing a game. The games may have either the form of Prisoner’s Dilemma or Hawk-Dove (Snow-Drift, Chicken) games, and may be considered as a competition for a benefit or resource. The result of each game gives each player a payoff, which is decreased from his amount of life. The advantage of such approach is that each player plays with different individuals separately, not as a multi-player matrix game. The probability for an individual having a certain action is considered his strategy, and each action returns a payoff to individual. The purpose of the work is test different fitness functions for evaluating the generation of new individuals, which will have characters of the best adapted individuals in a neighborhood, i.e., have higher values in a fitness function. Copyright Springer-Verlag Berlin Heidelberg 2015