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dc.contributor.authorKessler, Jaymin B
dc.date.accessioned2014-03-03T23:09:13Z
dc.date.available2014-03-03T23:09:13Z
dc.date.issued2004-12
dc.identifier.otherkessler_jaymin_b_200412_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/kessler_jaymin_b_200412_ms
dc.identifier.urihttp://hdl.handle.net/10724/22147
dc.description.abstractIn this thesis, we describe a genetic algorithm for optimizing the superpeer structure of semantic peer to peer networks. Peer to peer, also called P2P, networks enable us to search for content or information1 in a distributed fashion across a large number of peers while providing a level of fault tolerance by preventing disconnecting peers from disrupting the network. We seek to maximize the number of queries answered while minimizing the time in which they are answered. It will be shown that the genetic algorithm (GA) dramatically improves network performance and consistently finds networks better than those found by random search and hill climbing. A comparison will also be made to networks found through exhaustive search, showing that the GA will, for smaller networks, converge on a globally optimal solution.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectPeer to peer
dc.subjectGenetic algorithm
dc.subjectClustering
dc.subjectNetwork
dc.titleUsing genetic algorithms to reorganize superpeer structure in peer to peer networks
dc.typeThesis
dc.description.degreeMS
dc.description.departmentArtificial Intelligence
dc.description.majorArtificial Intelligence
dc.description.advisorKhaled Rasheed
dc.description.advisorBudak Arpinar
dc.description.committeeKhaled Rasheed
dc.description.committeeBudak Arpinar
dc.description.committeeWalter Potter
dc.description.committeeRonMcClendon


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