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Resource discovery is an essential problem in peer-to-peer networks since there is no centralized index in which 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, the search algorithms have been based on few simple rules, which often reduce the performance from optimal. In this paper, the authors describe the results of a process where evolutionary neural networks are used for finding an efficient search algorithm from a class of local search algorithms. The initial test results indicate that an evolutionary optimization process can produce search algorithm candidates that are competent compared to the Breadth First Search algorithm (BFS) used in Gnutella peer-to-peer network.
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