Multi-objective evolutionary optimization of computation-intensive simulations
- Autor(en)
- Elmar Kiesling, Bernhard Grill, Andreas Ekelhart, Christian Stummer, Christine Strauss
- Abstrakt
Expensive fitness functions, such as simulations, pose a challenge in optimization scenarios. Despite the use of metaheuristic optimization algorithms, simulation-optimization problems hence do not necessarily converge within reasonable runtime. We outlined a number of approaches to tackle this issue and will continue our experiments with these and other techniques. Our goal is to reduce the number of required simulation replications and the runtime spent evaluating each candidate solution in the context of information security control selection.
- Organisation(en)
- Institut für Rechnungswesen, Innovation und Strategie
- Externe Organisation(en)
- Technische Universität Wien, Secure Business Austria (SBA), Universität Bielefeld
- Publikationsdatum
- 2015
- Peer-reviewed
- Ja
- ÖFOS 2012
- 102016 IT-Sicherheit, 502050 Wirtschaftsinformatik, 101015 Operations Research
- Link zum Portal
- https://ucrisportal.univie.ac.at/de/publications/multiobjective-evolutionary-optimization-of-computationintensive-simulations(4e05419a-2023-49f1-9d8c-7aa6c07dc3aa).html