Self-adaptive Gossip Policies for Distributed Population-based Algorithms

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dc.creatorLaredo, J. L. J.
dc.creatorEiben, E. A.
dc.creatorSchoenauer, M.
dc.creatorCastillo, P. A.
dc.creatorMora, A. M.
dc.creatorFernandez, F.
dc.creatorMerelo, J. J.
dc.date2007-03-23
dc.date.accessioned2026-07-07T07:53:23Z
dc.date.available2026-07-07T07:53:23Z
dc.descriptionGossipping has demonstrate to be an efficient mechanism for spreading information among P2P networks. Within the context of P2P computing, we propose the so-called Evolvable Agent Model for distributed population-based algorithms which uses gossipping as communication policy, and represents every individual as a self-scheduled single thread. The model avoids obsolete nodes in the population by defining a self-adaptive refresh rate which depends on the latency and bandwidth of the network. Such a mechanism balances the migration rate to the congestion of the links pursuing global population coherence. We perform an experimental evaluation of this model on a real parallel system and observe how solution quality and algorithm speed scale with the number of processors with this seamless approach.
dc.descriptionSubmitted to Europar 2007
dc.identifierhttps://arxiv.org/abs/cs/0703117
dc.identifierhttp://arxiv.org/abs/cs/0703117
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/126235
dc.subjectDistributed, Parallel, and Cluster Computing
dc.titleSelf-adaptive Gossip Policies for Distributed Population-based Algorithms
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