Using GAs to estimate confidence intervals for missing spatial data

A technique for conditional spatial simulation using genetic algorithms is described. This technique can be used to characterize regions of missing data in regularly sampled data. The proposed technique is much faster than simulated annealing, the current state of the art in spatial simulation. An application of this technique for determining confidence intervals for missing data in optical measurements of gas turbines is discussed.