@inproceedings {pub3044,
	title = {Can Evolutionary Algorithms Beat Dynamic Programming for Hybrid Car Control?},
	author = {Tobias Rodemann AND Ken Nishikawa},
	year = {2016},
	month = {March},
	abstract = {Finding the best possible sequence of control actions for a hybrid car in order to minimize fuel consumption is a well-studied problem. A standard method is Dynamic Programming (DP) that is generally considered to provide solutions close to the global optimum in relatively short time. To our knowledge Evolutionary Algorithms (EAs) have so far not been used for this setting, due to the success of DP.  In this work we compare DP and EA for a well-studied example and find that for the basic scenario EA is indeed clearly outperformed by DP in terms of calculation time and quality of solutions. But, we also find that  when going beyond the standard scenario towards more realistic (and complex) scenarios, EAs can actually deliver a performance  en par or even exceeding DP, making them useful in a number of relevant application scenarios.},
	publisher = {Springer},
	booktitle = {Lecture Notes in Computer Science (EvoApplications Part I)},
	editor = {G. Squillero and P. Burelli},
	series = {LNCS 9597}
}