id: 05976339
dt: a
an: 05976339
au: Güdemann, Matthias; Ortmeier, Frank
ti: Model-based multi-objective safety optimization.
so: Flammini, Francesco (ed.) et al., Computer safety, reliability, and
    security. 30th international conference, SAFECOMP 2011, Naples, Italy,
    September 19‒22, 2011. Proceedings. Berlin: Springer (ISBN
    978-3-642-24269-4/pbk). Lecture Notes in Computer Science 6894, 423-436
    (2011).
py: 2011
pu: Berlin: Springer
la: EN
cc: 
ut: 
ci: 
li: doi:10.1007/978-3-642-24270-0_31
ab: Summary: 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.
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