  We consider the question of the stability of evolutionary algorithms to
gradual changes, or drift, in the target concept. We define an algorithm to be
resistant to drift if, for some inverse polynomial drift rate in the target
function, it converges to accuracy 1 -- \epsilon , with polynomial resources,
and then stays within that accuracy indefinitely, except with probability
\epsilon , at any one time. We show that every evolution algorithm, in the
sense of Valiant (2007; 2009), can be converted using the Correlational Query
technique of Feldman (2008), into such a drift resistant algorithm. For certain
evolutionary algorithms, such as for Boolean conjunctions, we give bounds on
the rates of drift that they can resist. We develop some new evolution
algorithms that are resistant to significant drift. In particular, we give an
algorithm for evolving linear separators over the spherically symmetric
distribution that is resistant to a drift rate of O(\epsilon /n), and another
algorithm over the more general product normal distributions that resists a
smaller drift rate.
  The above translation result can be also interpreted as one on the robustness
of the notion of evolvability itself under changes of definition. As a second
result in that direction we show that every evolution algorithm can be
converted to a quasi-monotonic one that can evolve from any starting point
without the performance ever dipping significantly below that of the starting
point. This permits the somewhat unnatural feature of arbitrary performance
degradations to be removed from several known robustness translations.
