supported by the Federal Ministry for Economic Af-
fairs and Climate Action (BMWK) on the basis of a
decision of the German Bundestag.
REFERENCES
Auger, A. and Hansen, N. (2005). A Restart CMA Evo-
lution Strategy With Increasing Population Size. In
Proceedings of the IEEE Congress on Evolutionary
Computation, volume 2, pages 1769–1776.
B
¨
ack, T. (1994). Parallel Optimization of Evolutionary Al-
gorithms. In Goos, G., Hartmanis, J., Leeuwen, J.,
Davidor, Y., Schwefel, H.-P., and M
¨
anner, R., editors,
Parallel Problem Solving from Nature — PPSN III,
volume 866 of Lecture Notes in Computer Science,
pages 418–427. Springer Berlin Heidelberg, Berlin,
Heidelberg.
B
¨
ack, T., Foussette, C., and Krause, P. (2013). Contempo-
rary Evolution Strategies. Natural Computing Series.
Springer Berlin, Heidelberg, Berlin, Heidelberg, 1st
ed. edition.
de Nobel, J., Vermetten, D., Wang, H., Doerr, C., and B
¨
ack,
T. (2021). Tuning as a Means of Assessing the Bene-
fits of New Ideas in Interplay with Existing Algorith-
mic Modules. Technical report.
Eiben, A. E. and Smit, S. K. (2011). Parameter tuning for
configuring and analyzing evolutionary algorithms.
Swarm and Evolutionary Computation, 1(1):19–31.
Fujii, G., Takahashi, M., and Akimoto, Y. (2018). CMA-
ES-based structural topology optimization using a
level set boundary expression-Application to optical
and carpet cloaks. Computer Methods in Applied Me-
chanics and Engineering, 332:624–643.
Grefenstette, J. (1986). Optimization of Control Parameters
for Genetic Algorithms. IEEE Transactions on Sys-
tems, Man, and Cybernetics, 16(1):122–128.
Hansen, N. (2009). Benchmarking a BI-Population CMA-
ES on the BBOB-2009 Function Testbed. In Pro-
ceedings of the 11th Annual Conference Compan-
ion on Genetic and Evolutionary Computation Con-
ference: Late Breaking Papers, ACM Conferences,
pages 2389–2396, New York, NY, USA. Association
for Computing Machinery.
Hansen, N. (2016). The CMA Evolution Strategy: A Tuto-
rial. Technical report.
Hansen, N., Finck, S., Ros, R., and Auger, A. (2009).
Real-Parameter Black-Box Optimization Benchmark-
ing 2009: Noiseless Functions Definitions. Technical
Report RR-6829, INRIA.
Hansen, N. and Ostermeier, A. (1996). Adapting Arbitrary
Normal Mutation Distributions in Evolution Strate-
gies: The Covariance Matrix Adaptation. In Proceed-
ings of the IEEE International Conference on Evolu-
tionary Computation, pages 312–317.
International Organization for Standardization (2007). ISO
21994:2007 - Passenger cars - Stopping distance
at straight-line braking with ABS - Open-loop test
method.
Jolliffe, I. T. (1986). Principal Component Analysis.
Springer eBook Collection Mathematics and Statis-
tics. Springer, New York, NY.
Kerschke, P., Preuss, M., Wessing, S., and Trautmann, H.
(2015). Detecting Funnel Structures by Means of Ex-
ploratory Landscape Analysis. In Proceedings of the
2015 Annual Conference on Genetic and Evolutionary
Computation, ACM Digital Library, pages 265–272,
New York, NY. Association for Computing Machin-
ery.
Kerschke, P., Preuss, M., Wessing, S., and Trautmann, H.
(2016). Low-Budget Exploratory Landscape Analysis
on Multiple Peaks Models. In Neumann, F., editor,
Proceedings of the Genetic and Evolutionary Compu-
tation Conference, ACM Digital Library, pages 229–
236, New York, NY, USA. Association for Computing
Machinery.
Kerschke, P. and Trautmann, H. (2019). Comprehen-
sive Feature-Based Landscape Analysis of Continu-
ous and Constrained Optimization Problems Using the
R-Package Flacco. In Bauer, N., Ickstadt, K., L
¨
ubke,
K., Szepannek, G., Trautmann, H., and Vichi, M., edi-
tors, Applications in Statistical Computing, Studies in
Classification, Data Analysis, and Knowledge Orga-
nization, pages 93–123. Springer.
Koch-D
¨
ucker, H.-J. and Papert, U. (2014). Antilock brak-
ing system (ABS). In Reif, K., editor, Brakes, Brake
Control and Driver Assistance Systems, Bosch profes-
sional automotive information, pages 74–93. Springer
Vieweg, Wiesbaden.
Lindauer, M., Eggensperger, K., Feurer, M., Biedenkapp,
A., Deng, D., Benjamins, C., Ruhkopf, T., Sass,
R., and Hutter, F. (2022). SMAC3: A Versatile
Bayesian Optimization Package for Hyperparameter
Optimization. Journal of Machine Learning Research,
23(54):1–9.
Long, F. X., van Stein, B., Frenzel, M., Krause, P., Gitterle,
M., and B
¨
ack, T. (2022). Learning the Characteris-
tics of Engineering Optimization Problems with Ap-
plications in Automotive Crash. In Proceedings of the
Genetic and Evolutionary Computation Conference,
GECCO ’22, New York, NY, USA. Association for
Computing Machinery.
Loshchilov, I. and Hutter, F. (2016). CMA-ES for Hyperpa-
rameter Optimization of Deep Neural Networks.
Lunacek, M. and Whitley, D. (2006). The Dispersion Met-
ric and the CMA Evolution Strategy. In Proceedings
of the 8th Annual Conference on Genetic and Evolu-
tionary Computation, page 477. Association for Com-
puting Machinery.
Mersmann, O., Bischl, B., Trautmann, H., Preuss, M.,
Weihs, C., and Rudolph, G. (2011). Exploratory Land-
scape Analysis. In Lanzi, P. L., editor, Proceedings of
the 13th Annual Conference on Genetic and Evolu-
tionary Computation, ACM Conferences, pages 829–
836, New York, NY, USA. ACM.
Mersmann, O., Preuss, M., and Trautmann, H. (2010).
Benchmarking Evolutionary Algorithms: Towards
Exploratory Landscape Analysis. In Schaefer, R.,
Cotta, C., Kołodziej, J., and Rudolph, G., editors, Par-
allel Problem Solving from Nature, PPSN XI, Lecture
Real-World Optimization Benchmark from Vehicle Dynamics: Specification of Problems in 2D and Methodology for Transferring
(Meta-)Optimized Algorithm Parameters
39