Dakota Reference Manual: fixed_seed

| Dakota Reference Manual Version 6.15 Explore and Predict with Confidence

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fixed_seed

Reuses the same seed value for multiple random sampling sets

## Specification

Alias: none

Argument(s): 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