  This paper analises distributed evolutionary computation based on the
Representational State Transfer (REST) protocol, which overlays a farming model
on evolutionary computation. An approach to evolutionary distributed
optimisation of multilayer perceptrons (MLP) using REST and language Perl has
been done. In these experiments, a master-slave based evolutionary algorithm
(EA) has been implemented, where slave processes evaluate the costly fitness
function (training a MLP to solve a classification problem). Obtained results
show that the parallel version of the developed programs obtains similar or
better results using much less time than the sequential version, obtaining a
good speedup.
