Several metaheuristic approaches for global optimization (GO) are investigated and their performances compared in this paper. We critically review and analyze recently proposed Stochastic Genetic Algorithm (StGA) for GO and compare it with our GO hybrid metaheuristic called Genetic LPτ and Simplex Search (GLPτS), which combines the effectiveness of Genetic Algorithms during the early stages of the search with the advantages of Low-Discrepancy sequences and the local improvement abilities of Simplex search. For comparison purposes we also use Fast Evolutionary Programming (FEP) and Differential Evolution (DE) methods. In parallel to our method, FEP and DE, we investigate further, re-run and test the StGA implementation on a number of multimodal mathematical functions. The obtained StGA results demonstrate inferior performance (compared with our GLPτS and DE methods), producing much worse than the reported in  results (with the only exception for the two-dimensional cases). We argue that the published in  accuracy and convergence speed results (given as number of function evaluations) are incorrect for most of the testing functions and investigate why the method is failing in those cases.