In poker, a player declares “all in” when he decides to bet all of his remaining chips on the cards in his hand. He then waits nervously while the remaining cards are dealt, knowing that he will soon either win big or lose all of his chips (“go bust”).

A similar gamble occurs when you apply some optimization approaches based on Design of Experiments (DOE) concepts. In this case, the actual objective function being minimized is evaluated at a predetermined set of design points. Then, a simple approximation of the objective function is developed by fitting an analytical function to these points. This approximate function is often called a *response surface* (also a *surrogate function*). The optimization search is then performed on the response surface, because evaluations of this simpler function are usually much quicker than evaluations of the actual objective function.

However, by defining all of your design evaluations ahead of time (going “all in”), you are risking that the corresponding response surface may not accurately represent the true objective function. If the surface fit is not accurate enough, then searching the response surface may not really give you the optimal design. In fact, it is common for an inaccurate response surface to completely mislead the optimization search, resulting in a very poor solution. So, while an accurate response surface could yield an optimized solution at lower cost than some other optimization approaches, a poorly fit surface may yield no useful results at all (you’ll “go bust”). Continue reading