  Dynamic Threshold Optimization (DTO) adaptively "compresses" the decision
space (DS) in a global search and optimization problem by bounding the
objective function from below. This approach is different from "shrinking" DS
by reducing bounds on the decision variables. DTO is applied to Schwefel's
Problem 2.26 in 2 and 30 dimensions with good results. DTO is universally
applicable, and the author believes it may be a novel approach to global search
and optimization.
