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Self-adaptive Gossip Policies for Distributed Population-based Algorithms

J. L. J. Laredo·E. A. Eiben·M. Schoenauer·P. A. Castillo·A. M. Mora·F. Fernandez·J. J. Merelo
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Abstract

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.

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J. L. J. Laredo, E. A. Eiben, M. Schoenauer, P. A. Castillo, A. M. Mora, F. Fernandez, J. J. Merelo. 2007-03-23. Self-adaptive Gossip Policies for Distributed Population-based Algorithms. https://arxiv.org/abs/cs/0703117

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