Evolutionary P2P Networking That Fuses Evolutionary Computation and P2P Networking Together

In the present paper, we propose an evolutionary P2P networking technique that dynamically and adaptively optimizes several P2P network topologies, in which all of the nodes are included at the same time, in an evolutionary manner according to given evaluation criteria. In addition, through simulations, we examine whether the proposed evolutionary P2P networking technique can provide reliable search capability in dynamic P2P environments. In simulations, we assume dynamic P2P environments in which each node leaves and joins the network with its own probability and in which search objects vary with time. The simulation results show that topology reconstruction by the evolutionary P2P networking technique is better than random topology reconstruction when only a few types of search objects are present in the network at any moment and these search objects are not replicated. Moreover, for the scenario in which the evolutionary P2P networking technique is more effective, we show through simulations that when each node makes several links with other nodes in a single network topology, the evolutionary P2P networking technique improves the reliable search capability. Finally, the number of links that yields more reliable search capability appears to depend on how often nodes leave and join the network.