Hierarchically Performed Hazard Origin and Propagation Studies
SAFECOMP '99 Proceedings of the 18th International Conference on Computer Computer Safety, Reliability and Security
Some Methods for Nonlinear Multi-objective Optimization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
PRISM: Probabilistic Symbolic Model Checker
TOOLS '02 Proceedings of the 12th International Conference on Computer Performance Evaluation, Modelling Techniques and Tools
Safety Optimization: A Combination of Fault Tree Analysis and Optimization Techniques
DSN '04 Proceedings of the 2004 International Conference on Dependable Systems and Networks
Faster convergence by means of fitness estimation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Early quality prediction of component-based systems - A generic framework
Journal of Systems and Software
Probabilistic Model-Checking Support for FMEA
QEST '07 Proceedings of the Fourth International Conference on Quantitative Evaluation of Systems
Model Based Importance Analysis for Minimal Cut Sets
ATVA '08 Proceedings of the 6th International Symposium on Automated Technology for Verification and Analysis
A Framework for Qualitative and Quantitative Formal Model-Based Safety Analysis
HASE '10 Proceedings of the 2010 IEEE 12th International Symposium on High-Assurance Systems Engineering
The ins and outs of the probabilistic model checker MRMC
Performance Evaluation
Safety, Dependability and Performance Analysis of Extended AADL Models
The Computer Journal
Designing safe, reliable systems using scade
ISoLA'04 Proceedings of the First international conference on Leveraging Applications of Formal Methods
EDCC'05 Proceedings of the 5th European conference on Dependable Computing
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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It is well-known that in many safety critical applications safety goals are antagonistic to other design goals or even antagonistic to each other. This is a big challenge for the system designers who have to find the best compromises between different goals. In this paper, we show how model-based safety analysis can be combined with multi-objective optimization to balance a safety critical system wrt. different goals. In general the presented approach may be combined with almost any type of (quantitative) safety analysis technique. For additional goal functions, both analytic and black-box functions are possible, derivative information about the functions is not necessary. As an example, we use our quantitative model-based safety analysis in combination with analytical functions describing different other design goals. The result of the approach is a set of best compromises of possible system variants. Technically, the approach relies on genetic algorithms for the optimization. To improve efficiency and scalability to complex systems, elaborate estimation models based on artificial neural networks are used which speed up convergence. The whole approach is illustrated and evaluated on a real world case study from the railroad domain.