UEGO is a general clustering technique capable of accelerating and/or parallelizing existing search methods. UEGO is an abstraction of GAS, a genetic algorithm (GA) with subpopulation support, so the niching (i.e. clustering) technique of GAS can be applied along with any kind of optimizers, not only genetic algorithm. The aim of this paper is to analyze the behavior of the algorithm as a function of different parameter settings and types of functions and to examine its reliability with the help of Csendes' method. Comparisons to other methods are also presented.