论文标题
网络上的分布式体现的进化
Distributed Embodied Evolution over Networks
论文作者
论文摘要
在几个网络问题中，在部署之前，未知代理（即网络节点）的最佳行为。此外，可能需要代理来适应，即根据环境条件改变其行为。在这些情况下，离线优化通常是昂贵且效率低下的，而在线方法可能更合适。在这项工作中，我们使用分布式体现的进化方法来优化空间分布式，本地交互的代理，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某个任务。我们在几种测试方案上的结果表明，通过与邻居进行行为参数的交叉执行的局部信息交换使网络可以比不允许局部交互的情况更有效地进行优化过程，即使在每个代理商社区内的最佳行为参数上存在较大差异。
In several network problems the optimum behavior of the agents (i.e., the nodes of the network) is not known before deployment. Furthermore, the 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 use a distributed Embodied Evolution 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 results on several test scenarios show that the local exchange of information, performed by means of crossover of behavior parameters with neighbors, allows the network to conduct the optimization process more efficiently than the cases where local interactions are not allowed, even when there are large differences on the optimal behavior parameters within each agent's neighborhood.
