  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.
