Dynamic search initialisation strategies for multi-objective optimisation in peer-to-peer networks
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Resilience to churn of a peer-to-peer evolutionary algorithm
International Journal of High Performance Systems Architecture
EvAg: a scalable peer-to-peer evolutionary algorithm
Genetic Programming and Evolvable Machines
Distributing evolutionary computation in a networked multi-agent environment
Mathematical and Computer Modelling: An International Journal
SofEA: a pool-based framework for evolutionary algorithms using CouchDB
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Designing and testing a pool-based evolutionary algorithm
Natural Computing: an international journal
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In this work we propose a fine grained approach with self-adaptive migration rate for distributed evolutionary computation. Our target is to gain some insights on the effects caused by communication when the algorithm scales. To this end, we consider a set of basic topologies in order to avoid the overlapping of algorithmic effects between communication and topological structures. We analyse the approach viability by comparing how solution quality and algorithm speed change when the number of processors increases and compare it with an Island model based implementation. A finer-grained approach implies a better chance of achieving a larger scalable system; such a feature is crucial concerning large-scale parallel architectures such as peer-to-peer systems. In order to check scalability, we perform a threefold experimental evaluation of this model: first, we concentrate on the algorithmic results when the problem scales up to eight nodes in comparison with how it does following the Island model. Second, we analyse the computing time speedup of the approach while scaling. Finally, we analyse the network performance with the proposed self-adaptive migration rate policy that depends on the link latency and bandwidth. With this experimental setup, our approach shows better scalability than the Island model and a equivalent robustness on the average of the three test functions under study.