The Pointer technique consists of a complementary set of
optimization algorithms:
One of the problems with optimization is that “there is nothing you
can say about an arbitrary system.” This statement is correct to some
extent. However, the following observations can be made. First, a system
can be classified once more is known about it. Second, once classified,
there is some combination of optimization methods that would work better
than random guessing.
Knowing the type of system you have is critical to being able to solve
the system efficiently. Optimization theory has always been developed
the other way around. Assuming that the system has a certain mathematical
form, what is the most cost efficient way of optimizing this system?
Over time, a large collection of optimization methods based on such assumptions
were developed. Each of these methods had many degrees of freedom to
adapt the method to the problem at hand. In essence, the specific problem
that people wanted to solve (e.g., design a lighter structure) was now
transformed into finding the right algorithm for designing the structure.
This second task was in many ways a harder task than the original. For
many engineers and scientists, the problem was transformed from a known
to an unknown domain. As a consequence, optimization technology did not
become the big commercial success for which many had hoped.
Therefore, the issue of control became a driving factor in the development
of the Pointer optimization engine. The Pointer technique uses a proprietary
algorithm that automatically controls a set of optimization resources.
Similarly, the Pointer technique efficiently solves a wide range of problems
in a fully automatic manner by harnessing and leveraging the power of
a group of distinct complementary optimization algorithms.