AutoRSM

AutoRSM is an automation and extension of the techniques that would be used by a statistician applying response surface methodolgy. In the basic RSM method, experiments are taken in a certain region of interest in order to obtain a local model of the effects of the input variables on the outputs. These parameter settings of these experiments are chosen in order to maximize the information gained from the experiment. Once a particular region of interest is well understood, a decision is made. Either we believe that a local optimum lies within the current region of interest, in which case we give the optimum, or we move the region of interest to a new area that it expected to yield better results.

We can now describe the AutoRSM algorithm:

1. Choose an initial base point (center of region of interest).

2. Check to see if we have enough information to follow a gradient to a new region of interest. If so, move the base point to the new region of interest and suggest an experiment in the new region of interest. The decision of where to move the base includes checking for quadratic ridges and valleys in order to find the direction of movement that will be most efficient in getting to the optimum. Go to step 5.

3. Check to see if we have enough information to say that there is a local optimum within this region of interest. Suggest an experiment at or near the optimum. We may choose an experiment near rather than at the optimum in order to get more information to more precisely identify its location. Go to step 5.

4. Since we will not be moving the base yet, suggest the experiment that will add the most information about our current estimate of the gradient at the base point. Go to step 5.

5. Check for stopping criteria. Examples include a fixed number of experiments, or the identification of a local optimum. If the criteria is not met, repeat to step 2.