Abstract
In this paper, the power of genetic algorithms is exploited to search for the most important groups of events in a risk/reliability model. To this end a two-objective search is formulated in which the decision variables are the groups of events, and the objectives are to maximize the importance of the groups, while minimizing their dimension. This allows the identification of the most important single events, couples of events, triplets, and so forth. Three case studies concerning network systems of increasing complexity are analysed.
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