Genetic algorithms and scenario reduction

Scenario reduction is designed for selecting a rep resentative subset of geostatistical simulations out of a much larger set of simulations. Three steps are involved: measuring the dissimilarity between two s imulations; finding a metric to measure the distance between any subset of k simula tions and the full set of N simulations and finding an efficient algorithm for selecting the subset that minimizes the metric. This paper focuses on the thi rd question. We show that genetic algorithms are an efficient way of approach ing the minimum when the population of subsets to be sampled is very large. Two case-studies are presented.

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