As a population evolves,
its members are under selection both for rate of reproduction
(fitness) and mutational robustness. For those using evolutionary
algorithms
as optimisation techniques,
this second selection pressure can sometimes be beneficial, but it
can also bias evolution in unwelcome and unexpected ways. Here, the
role of selection for mutational robustness in driving adaptation on
neutral networks is explored. The behaviour of a standard genetic
algorithm
is compared with that of a search
algorithm designed to be immune to selection for mutational
robustness. Performance on an RNA folding landscape suggests that
selection for mutational robustness, at least sometimes, will not
unduly retard the rate of evolutionary innovation enjoyed by a
genetic algorithm. Two classes of random landscape are used to
explore the reasons for this result.