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Evolutionary algorithms have been shown to obtain good solutions for complexoptimization problems in static and dynamic environments. It is important tounderstand the behaviour of evolutionary algorithms for complex optimizationproblems that also involve dynamic and/or stochastic components in a systematicway in order to further increase their applicability to real-world problems. Weinvestigate the node weighted traveling salesperson problem (W-TSP), whichprovides an abstraction of a wide range of weighted TSP problems, in dynamicsettings. In the dynamic setting of the problem, items that have to becollected as part of a TSP tour change over time. We first present a dynamicsetup for the dynamic W-TSP parameterized by different types of changes thatare applied to the set of items to be collected when traversing the tour. Ourfirst experimental investigations study the impact of such changes on resultingoptimized tours in order to provide structural insights of optimizationsolutions. Afterwards, we investigate simple mutation-based evolutionaryalgorithms and study the impact of the mutation operators and the use ofpopulations with dealing with the dynamic changes to the node weights of theproblem.

On the Impact of Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson Problem