Analysis and Handling of Dynamic Problem Changes in Open-Ended Optimization

Changes in dynamic optimization problems entail updates to the problem model, which in turn can result in changes to the problem's fitness landscape and even its solution encoding. In order to yield valid solutions that are applicable to the current problem state, optimization algorithms must be able to cope with such dynamic problem updates. Furthermore, depending on the optimization use case, changes occurring in real-world environments require an optimizer to adapt to changing process conditions and yield updated, valid solutions within a short time frame. In this paper, dynamic problem changes and their effects on an optimizer's algorithmic behavior are studied in the context of crane scheduling. Three open-ended versions of RAPGA, a relevant alleles preserving genetic algorithm, are evaluated, some of which include self-adaption and a special treatment of certain events that require domain knowledge to be recognized. The proposed extensions affect the algorithm behavior as desired. On the one hand, the algorithms converge faster after a loss in solution quality is detected. On the other hand, new genetic material is introduced, making it possible to reach high quality areas of the search space again.