fixed_seed
Reuses the same seed value for multiple random sampling sets
Specification
Alias: None
Arguments: None
Default: not fixed; pattern varies run-to-run
Description
The fixed_seed
flag is relevant if multiple sampling sets will be
generated over the coarse of a Dakota analysis. This occurs when using
advance methods (e.g., surrogate-based optimization, optimization
under uncertainty). The same seed value is reused for each of these
multiple sampling sets, which can be important for reducing
variability in the sampling results.
Default Behavior
The default behavior is to not use a fixed seed, as the repetition of
the same sampling pattern can result in a modeling weakness that an
optimizer could potentially exploit (resulting in actual reliabilities
that are lower than the estimated reliabilities). For repeatable
studies, the seed
must also be specified.
Examples
method
sampling
sample_type lhs
samples = 10
fixed_seed