Abstract
An intrinsic characteristic of stochastic optimization methods, such as simulated annealing, genetic algorithms and multi-start hill climbing, is that they can be run again and again on the same inputs, each time potentially producing a different answer. When such algorithms are used in a design process with multiple levels of abstraction, where the output of one stochastic optimizer becomes the problem statement for another stochastic optimizer, we get an implicit tree of alternative designs. After each optimizer run we face a control problem of which level's optimizer to run next, and which design alternative to run it on. This problem is made more difficult by the fact that we generally can get a precise evaluation of the design alternatives only at the lowest level (the final results), and must make do at higher levels with only an estimate of how good a final design each alternative will lead to.