  Evolutionary algorithms have been shown to obtain good solutions for complex
optimization problems in static and dynamic environments. It is important to
understand the behaviour of evolutionary algorithms for complex optimization
problems that also involve dynamic and/or stochastic components in a systematic
way in order to further increase their applicability to real-world problems. We
investigate the node weighted traveling salesperson problem (W-TSP), which
provides an abstraction of a wide range of weighted TSP problems, in dynamic
settings. In the dynamic setting of the problem, items that have to be
collected as part of a TSP tour change over time. We first present a dynamic
setup for the dynamic W-TSP parameterized by different types of changes that
are applied to the set of items to be collected when traversing the tour. Our
first experimental investigations study the impact of such changes on resulting
optimized tours in order to provide structural insights of optimization
solutions. Afterwards, we investigate simple mutation-based evolutionary
algorithms and study the impact of the mutation operators and the use of
populations with dealing with the dynamic changes to the node weights of the
problem.
