@inproceedings {pub2811,
	title = {Many-Objective Optimization of a Hybrid Car Controller},
	author = {Tobias Rodemann AND Kaname Narukawa AND Michael Fischer AND Mohammed Awada},
	year = {2015},
	month = {April},
	abstract = {Hybrid cars are considered to be a promising approach for providing individual mobility with lower CO -emissions without compromising on affordability and driving range. In order to reach these targets a highly efficient control (energy management) is required. In mass production vehicles control is often organized using simple, quick and easy to understand rule-based systems. Such a rule-base typically contains a moderate number of parameters which can be tuned using methods like evolutionary algorithms to improve performance. However, prior work basically targets a minimization of fuel consumption. In this work we present a many-objective evolutionary optimization that considers up to 7 objectives in parallel. We outline the additional optimization challenges that arise due to the large number of objectives and demonstrate that a substantial performance increase, over all objectives, can be achieved if one is willing to accept a moderate performance loss in the primary objective (fuel consumption).},
	publisher = {Springer},
	booktitle = {EvoStar 2015 Conference}
}