Reliability and performance of UEGO, a clustering-based global optimizer

UEGO is a general clustering technique capable of accelerating and/or paral lelizing 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 onl y genetic algorithm. The aim of this paper is to analyze the behavior of th e algorithm as a function of different parameter settings and types of func tions and to examine its reliability with the help of Csendes' method. Comp arisons to other methods are also presented.