Dakota Reference Manual: exploration

| Dakota Reference Manual Version 6.15 Explore and Predict with Confidence

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exploration

Portion of batch size dedicated to exploration in parallel EGO

## Specification

Alias: none

Argument(s): INTEGER

## Description

Refinement candidates are generated by an acquisition function such as maximum expected improvement, which balances exploration and exploitation. Refinement candidates can also be generated by purely explorative metrics such as maximum prediction variance. For a specified `batch_size`, `exploration` specifies the subset of this total that will be dedicated to pure exploration of the parameter space.

Default Behavior All of the batch size is devoted to the standard acquisition approach, balancing exploration and exploitation.

## Examples

    method,
            efficient_global
          seed = 1237
          batch_size = 8    # total
          exploration = 2   # 2 out of 8