  We present a genetic algorithm which is distributed in two novel ways: along
genotype and temporal axes. Our algorithm first distributes, for every member
of the population, a subset of the genotype to each network node, rather than a
subset of the population to each. This genotype distribution is shown to offer
a significant gain in running time. Then, for efficient use of the
computational resources in the network, our algorithm divides the candidate
solutions into pipelined sets and thus the distribution is in the temporal
domain, rather that in the spatial domain. This temporal distribution may lead
to temporal inconsistency in selection and replacement, however our experiments
yield better efficiency in terms of the time to convergence without incurring
significant penalties.
