In many cases there is a requirement for an algorithm to accomplish a task and the existence of a metric to judge the success of the algorithm. In most cases, a priori knowledge of a technique for obtaining a working algorithm does not exist. A typical case is aimpoint selection given the profile of the target. For a weapons system to obtain a kill it must hit the aimpoint within a critical distance, but algorithms for the automatic selection of aimpoints do not exist. Human beings can examine the data available to the weapons system offline and correlate the data with the desired aimpoint, but the construction of an algorithm which uses the data to determine the aimpoint is non-trivial.
Similar problems exist in the area of control.
Here a simulation of the system exists and a desired degree of stability must be obtained. Typically, the problem is linearized in a number of different portions of the parameter space and solved. These linear solutions are then used as the starting point for creation of an adaptive Kalman filter which is then refined for each segment of the parameter space. The technique is difficult and labor intensive and relies on the ability of an scientist or engineer to achieve good starting points in the parameter space. In these cases, a metric for success is available and the GA technique uses a large number of strings to perform a metric driven parallel search of the parameter space to achieve a near optimal solution.