We present a genetic algorithm which is distributed in two

novel ways: along genotype and temporal axes. Our algo-

rithm fi rst 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 distri-

bution is shown to off er a signifi cant gain in running time.

Then, for effi cient 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 effi -

ciency in terms of the time to convergence without incurring

signifi cant penalties.