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In solving unconstrained global optimization (UGO) problems, devising nonlinear programming (NLP) methods based on gradient information are extremely difficult when an objective function is non-differential. As a stochastic global optimization algorithm, particle swarm optimization (PSO) algorithm does not require gradient information, enabling it to overcome the limitation of traditional NLP schemes. Unfortunately, performance of a PSO algorithm depends on several parameters, such as constriction coefficient, cognitive parameter and social parameter. To overcome the above limitations of a PSO algorithm, this work presents a real-coded genetic algorithm (RGA)-based PSO (RGA-PSO) algorithm. The specific parameters of the inner PSO algorithm are optimized using the outer RGA. Performance of the proposed RGA-PSO algorithm is then evaluated using a set of UGO problems. Numerical results indicate in addition to its ability to converge to a global minimum for each test UGO problem, the proposed RGA-PSO algorithm provides a solution is more precise than those of some stochastic global optimization algorithms. Thus, the RGA-PSO algorithm can be considered as an alternative stochastic global optimization scheme for solving UGO problems.