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In case the objective function to be minimized is not known analytically and no assumption can be made about the single extremum, global optimization (GO) methods must be used. Paper gives a brief overview of GO methods, with the special attention to principles of clustering, covering and evolution. Nine algorithms, including a simple GA, are compared in terms of effectiveness (accuracy), efficiency (number of the needed function evaluations) and reliability on several problems. Particular features of adaptive cluster covering algorithm (ACCO) leading to its high efficiency are analyzed and compared with those of an evolutionary approach. The possibilities of (partially) attributing ACCO and other GO algorithms to the group of EA are considered.