Abstract
Kriging is a popular method for estimating the global optimum of a simulated system. Kriging approximates the input/output function of the simulation model. Kriging also estimates the variances of the predictions of outputs for input combinations not yet simulated. These predictions and their variances are used by efficient global optimization"(EGO), to balance local and global search. This article focuses on two related questions: (1) How to select the next combination to be simulated when searching for the global optimum? (2) How to derive confidence intervals for outputs of input combinations not yet simulated? Classic Kriging simply plugs the estimated Kriging parameters into the formula for the predictor variance, so theoretically this variance is biased. This article concludes that practitioners may ignore this bias, because classic Kriging gives acceptable confidence intervals and estimates of the optimal input
combination. This conclusion is based on bootstrapping and conditional
simulation.
combination. This conclusion is based on bootstrapping and conditional
simulation.
| Original language | English |
|---|---|
| Place of Publication | Tilburg |
| Publisher | Information Management |
| Number of pages | 22 |
| Volume | 2014-076 |
| Publication status | Published - 2 Dec 2014 |
Publication series
| Name | CentER Discussion Paper |
|---|---|
| Volume | 2014-076 |
Keywords
- simulaiton
- Optimization
- Kriging
- Bootstrap
- Conditional simulation