  Niching enables a genetic algorithm (GA) to maintain diversity in a
population. It is particularly useful when the problem has multiple optima
where the aim is to find all or as many as possible of these optima. When the
fitness landscape of a problem changes overtime, the problem is called
non--stationary, dynamic or time--variant problem. In these problems, niching
can maintain useful solutions to respond quickly, reliably and accurately to a
change in the environment. In this paper, we present a niching method that
works on the problem substructures rather than the whole solution, therefore it
has less space complexity than previously known niching mechanisms. We show
that the method is responding accurately when environmental changes occur.
