In most network problems, the optimum behaviors of agents in the network are not known before deployment. In addition to that, agents might be required to adapt, i.e. change their behavior based on the environment conditions. In these scenarios, offline optimization is usually costly and inefficient, while online methods might be more suitable. In this work we propose a distributed embodied evolutionary approach to optimize spatially distributed, locally interacting agents by allowing them to exchange their behavior parameters and learn from each other to adapt to a certain task within a given environment. Our numerical results show that the local exchange of information, performed by means of crossover of behavior parameters with neighbors, allows the network to converge to the global optimum more efficiently than the cases where local interactions are not allowed, even when there are large differences on the optimal behaviors within a neighborhood.

中文翻译：

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代理网络中的分布式体现进化

在大多数网络问题中，网络中代理的最佳行为在部署之前是未知的。除此之外，代理可能需要适应，即根据环境条件改变他们的行为。在这些场景中，离线优化通常成本高且效率低，而在线方法可能更合适。在这项工作中，我们提出了一种分布式体现进化方法，通过允许它们交换其行为参数并相互学习以适应给定环境中的特定任务来优化空间分布的局部交互代理。我们的数值结果表明，通过行为参数与邻居的交叉进行的本地信息交换，