  Evolutionary algorithms are bio-inspired algorithms that can easily adapt to
changing environments. Recent results in the area of runtime analysis have
pointed out that algorithms such as the (1+1)~EA and Global SEMO can
efficiently reoptimize linear functions under a dynamic uniform constraint.
Motivated by this study, we investigate single- and multi-objective baseline
evolutionary algorithms for the classical knapsack problem where the capacity
of the knapsack varies over time.
  We establish different benchmark scenarios where the capacity changes every
$\tau$ iterations according to a uniform or normal distribution. Our
experimental investigations analyze the behavior of our algorithms in terms of
the magnitude of changes determined by parameters of the chosen distribution,
the frequency determined by $\tau$, and the class of knapsack instance under
consideration. Our results show that the multi-objective approaches using a
population that caters for dynamic changes have a clear advantage on many
benchmarks scenarios when the frequency of changes is not too high.
Furthermore, we demonstrate that the diversity mechanisms used in popular
evolutionary multi-objective algorithms such as NSGA-II and SPEA2 do not
necessarily result in better performance and even lead to inferior results
compared to our simple multi-objective approaches.

Neural and Evolutionary Computing