  Genetic algorithms (GAs) that solve hard problems quickly, reliably and
accurately are called competent GAs. When the fitness landscape of a problem
changes overtime, the problem is called non--stationary, dynamic or
time--variant problem. This paper investigates the use of competent GAs for
optimizing non--stationary optimization problems. More specifically, we use an
information theoretic approach based on the minimum description length
principle to adaptively identify regularities and substructures that can be
exploited to respond quickly to changes in the environment. We also develop a
special type of problems with bounded difficulties to test non--stationary
optimization problems. The results provide new insights into non-stationary
optimization problems and show that a search algorithm which automatically
identifies and exploits possible decompositions is more robust and responds
quickly to changes than a simple genetic algorithm.
