  This paper extends previous work done by Tanese on the distributed genetic
algorithm (DGA). Tanese found that the DGA outperformed the canonical serial
genetic algorithm (CGA) on a class of difficult, randomly-generated Walsh
polynomials. This left open the question of whether the DGA would have similar
success on functions that were more amenable to optimization by the CGA. In
this work, experiments were done to compare the DGA's performance on the Royal
Road class of fitness functions to that of the CGA. Besides achieving
superlinear speedup on KSR parallel computers, the DGA again outperformed the
CGA on the functions R3 and R4 with regard to the metrics of best fitness,
average fitness, and number of times the optimum was reached. Its performance
on R1 and R2 was comparable to that of the CGA. The effect of varying the DGA's
migration parameters was also investigated. The results of the experiments are
presented and discussed, and suggestions for future research are made.
