Self-adaptive Gossip Policies for Distributed Population-based Algorithms
<|reference_start|>Self-adaptive Gossip Policies for Distributed Population-based Algorithms: Gossipping 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.<|reference_end|>
@article{laredo2007self-adaptive,
    title={Self-adaptive Gossip Policies for Distributed Population-based
  Algorithms},
    author={J.L.J. Laredo, E.A. Eiben, M. Schoenauer, P.A. Castillo, A.M. Mora, F.
  Fernandez, J.J. Merelo},
    journal={arXiv preprint arXiv:cs/0703117},
    year={2007},
    archivePrefix={arXiv},
    eprint={cs/0703117},
    primaryClass={cs.DC}
}