Last week we were presenting the work Studying the Cache Size in a Gossip-Based Evolutionary Algorithm [BibTex] in the 3rd International Symposium on Intelligent Distributed Computing hold in Ayia Napa (Cyprus).
Gossiping is a self-organized and decentralized approach to distribute algorithms through Peer-to-Peer (P2P) networks.
Based on such an approach, the Evolvable Agent model is a P2P Evolutionary Algorithm (EA) whose
population structure is defined by the gossiping protocol newscast, a protocol that behaves asymptotically as a small-world graph. This paper explores the impact of different cache sizes on the algorithm performance given that cache size is the only tunable parameter in newscast. To this aim, the problem generator wP-PEAKS and the multimodal deceptive problem MMDP have been used as benchmarks.
Results show that the quality of the solutions and the run-time of the algorithm are not altered when changing the settings of the cache size. This fact points out that newscast is a robust gossiping protocol for tackling distributed evolutionary computation.
GeNeura Team 