Abstract Evolutionary games usually take into consideration individuals’ strategies as the transformative characteristic which leads to the evolution of the population. Here, besides the strategies, interaction aspects are also considered as evolutionary attributes which can change over time as the replacement dynamic renovates the population choosing locally better individuals to reproduce. The population is modeled by cellular automata, interactions by the Prisoner’s Dilemma game and the replacement process is ruled by two versions of death-birth dynamic. Although the average payoff per game is considered as the fitness for choosing better individuals, the number of games per time step and a maximum radius of interaction with neighbours are also present in the individual’s chromosome which is passed to the next generation. Numerical simulations show that individual interaction properties and cooperation level are linked to the version of death-birth dynamic used and the game payoff. For instance, when the fitness bias is on the death event, individuals have more interactions in a larger radius, and the cooperation level is usually lower than the case where the fitness bias is on the birth event. Also, the individuals’ interaction profiles are heterogeneous, and cooperative individuals form clusters in the lattice to protect themselves.

中文翻译：

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连续概率元胞自动机和进化算法建模的群体中空间囚徒困境的作为进化特征的交互特征

摘要 进化博弈通常将个体的策略作为导致种群进化的变革特征。在这里，除了策略之外，交互方面也被视为进化属性，随着替换动态更新种群，选择本地更好的个体进行繁殖，它们会随着时间的推移而改变。人口由元胞自动机建模，囚徒困境游戏的相互作用和替换过程由两个版本的生死动态控制。虽然每场比赛的平均收益被认为是选择更好个体的适应度，但每个时间步长的比赛次数和与邻居的最大互动半径也存在于传递给下一代的个体染色体中。数值模拟表明，个体交互属性和合作水平与所使用的生死动态版本和博弈收益有关。例如，当适应度偏差在死亡事件上时，个体在更大的半径上有更多的交互，合作水平通常低于适应度偏差在出生事件上的情况。此外，个体的交互特征是异质的，合作个体在格子中形成集群以保护自己。并且合作水平通常低于适应度偏差在出生事件上的情况。此外，个体的交互特征是异质的，合作个体在格子中形成集群以保护自己。并且合作水平通常低于适应度偏差在出生事件上的情况。此外，个体的交互特征是异质的，合作个体在格子中形成集群以保护自己。