The distributed genetic algorithm used in the experiments so far has shown results inferior to other methods. This is possibly due to optimizing a small number of evaluation coefficient groups within GNUchess and using a small population size. The limited success may also be because of optimizing a limited number of parameters or that the original GNUchess evaluation coefficients are already close to optimal.

1. Conclusion Part II.
Currently, our results seem inferior to the improvement
obtained with other means in table 1–3 . In the experiments executed thus far
in this project, the distributed genetic algorithm manipulates
the evaluation-coefficients within the GNUchess
program in a smaller number (6 or 12) of groups
and uses a small population for optimization. As points out, the limited success
of our past experiments
may have its cause in the limited number of
parameters which we optimize, or simply the fact that
the original GNU-chess program has a family of builtin
evaluation-coefficients which are close to optimal
within the search space.8