  Evolutionary computation has shown its superiority in dynamic optimization,
but for the (dynamic) time-linkage problems, some theoretical studies have
revealed the possible weakness of evolutionary computation. Since the
theoretically analyzed time-linkage problem only considers the influence of an
extremely strong negative time-linkage effect, it remains unclear whether the
weakness also appears in problems with more general time-linkage effects.
Besides, understanding in depth the relationship between time-linkage effect
and algorithmic features is important to build up our knowledge of what
algorithmic features are good at what kinds of problems. In this paper, we
analyze the general time-linkage effect and consider the time-linkage OneMax
with general weights whose absolute values reflect the strength and whose sign
reflects the positive or negative influence. We prove that except for some
small and positive time-linkage effects (that is, for weights $0$ and $1$),
randomized local search (RLS) and (1+1)EA cannot converge to the global optimum
with a positive probability. More precisely, for the negative time-linkage
effect (for negative weights), both algorithms cannot efficiently reach the
global optimum and the probability of failing to converge to the global optimum
is at least $1-o(1)$. For the not so small positive time-linkage effect
(positive weights greater than $1$), such a probability is at most $c+o(1)$
where $c$ is a constant strictly less than $1$.
