Characterizing environmental changes in Robust Optimization Over Time

Evolutionary dynamic optimization has been drawing more and more research attention, and yet most work in this area is focused on Tracking Moving Optimum (TMO), which is to optimize the current fitness function at any time point. Recently, we proposed a more practical way to solve dynamic optimization problems, which is referred to as Robust Optimization Over Time (ROOT). In ROOT, we are trying to find solutions whose performances are acceptable over more than one environmental state, i.e., fitness functions. Before any development of benchmarks or algorithms for ROOT, it is necessary to have some understanding of what aspects of an environment can change and more importantly how these changes influence the solving of ROOT problems. In this paper, we develop a number of measures which can be used to characterize and analyse the underlying changing environment in the framework of ROOT. We test these measures on several benchmark problem instances, and it is shown that these measures are able to differentiate different dynamics effectively and provide useful information about what kind of algorithms might or might not suit certain dynamic environments.