@inproceedings {pub4102,
	title = {An Adversarial Optimization Approach for the Development of Robust Controllers},
	author = {Baraq Mushtaq AND Tobias Rodemann},
	year = {2020},
	month = {April},
	abstract = {Due to the increasing popularity of electric vehicles (EVs) there is rising demand for smart controller solutions that optimize the flow of energy between buildings and electric vehicles. Simple rule-based controllers are (often manually) developed and tuned for specific use case scenarios, for example a single family home with home and mobility usage patterns and country-specific regulations. However, it is often very difficult to correctly anticipate the exact conditions the controller has to work on so that a high performance under worst case conditions is a very important target. In this work we use an adversarial optimization approach in order to find both challenging scenarios and controller parameterizations that perform well in those scenarios. We can show that in comparison to a standard controller approach our approach can find challenging scenarios for the standard controller and controllers that perform better on those worst case scenarios.},
	publisher = {Springer, LNCS, LNAI, LNBI},
	booktitle = {Applications of Evolutionary Computation. EvoApplications 2020},
	editor = { Castillo P., Jim{\'e}nez Laredo J., Fern{\'a}ndez de Vega F},
	series = {Lecture Notes in Computer Science}
}