@inproceedings{SpettelBeyerHellwig2019,
author = {Spettel, Patrick and Beyer, Hans-Georg and Hellwig, Michael},
title = {Steady state analysis of a multi-recombinative meta-ES on a conically constrained problem with comparison to σSA and CSA},
series = {FOGA '19. Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms},
booktitle = {FOGA '19. Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms},
publisher = {ACM},
address = {New York, NY, USA},
isbn = {978-1-4503-6254-2},
doi = {10.1145/3299904.3340306},
pages = {43 -- 57},
year = {2019},
language = {en}
}
@inproceedings{Finck2019,
author = {Finck, Steffen},
title = {Worst case search over a set of forecasting scenarios applied to financial stress-testing},
series = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},
editor = {Lopez-Ibanez, Manuel and Auger, Anne and St{\"u}tzle, Thomas},
publisher = {ACM},
address = {New York, NY, USA},
isbn = {978-1-4503-6748-6},
doi = {10.1145/3319619.3326835},
pages = {1722 -- 1730},
year = {2019},
abstract = {Stress testing is part of today's bank risk management and often required by the governing regulatory authority. Performing such a stress test with stress scenarios derived from a distribution, instead of pre-defined expert scenarios, results in a systematic approach in which new severe scenarios can be discovered. The required scenario distribution is obtained from historical time series via a Vector-Autoregressive time series model. The worst-case search, i.e. finding the scenario yielding the most severe situation for the bank, can be stated as an optimization problem. The problem itself is a constrained optimization problem in a high-dimensional search space. The constraints are the box constraints on the scenario variables and the plausibility of a scenario. The latter is expressed by an elliptic constraint. As the evaluation of the stress scenarios is performed with a simulation tool, the optimization problem can be seen as black-box optimization problem. Evolution Strategy, a well-known optimizer for black-box problems, is applied here. The necessary adaptations to the algorithm are explained and a set of different algorithm design choices are investigated. It is shown that a simple box constraint handling method, i.e. setting variables which violate a box constraint to the respective boundary of the feasible domain, in combination with a repair of implausible scenarios provides good results.},
language = {en}
}