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A priori knowledge of the behavior of the aggregates is helpful for controlling the course of the algorithm such that chances for a successful run increase. However this behavior depends on the instance. It is therefore interesting to characterize instances with a number of parameters. These parameters do not have to be known at the start of the algorithm. They can be estimated ‘on the fly’ as long as these estimates have become accurate enough when they are used for deriving decisions. A study of the generic behavior of the aggregates is therefore useful. This behavior depends on the distribution of scores over the states. This distribution is not known in general, and consequently, we have to rely on assumptions and experiments to perform the kind of analysis necessary for discovering the parameters characterizing an instance. For the annealing applications that we have encountered the observations and conclusions of this chapter seem to be in excellent correspondence. However, applying these in implementations has to be done with care and in combination with generally valid criteria. The next chapter will take this task as its subject.
KeywordsNormal Density Aggregate Function Annealing Application Weak Control Score Density
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