Resource Discovery in P 2 P Networks Using Evolutionary Neural Networks

Resource discovery is an essential problem in peer-to-peer networks since there is no centralized index where to look for information about resources. One solution for the problem is to use a search algorithm that locates resources based on the local knowledge about the network. Traditionally, these search algorithms have been based on few predetermined rules. The problem with these algorithms is that if the conditions in the network change the algorithm becomes less efficient and won't adapt to the new environment. In this paper, we describe the results of a process where evolutionary neural networks are used for finding an efficient search algorithm. The initial test results indicate that an evolutionary optimization process can produce search algorithm candidates that are more efficient compared to breadth-first search algorithm (BFS) used in Gnutella peer-to-peer network.